International Journal of Mineral Processing 100 (2011) 57–71

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International Journal of Mineral Processing

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A review of froth flotation control

B.J. Shean, J.J. Cilliers ⁎Rio Tinto Centre for Advanced Mineral Recovery at Imperial College London, Department of Earth Science and Engineering, Imperial College London, SW7 2AZ, United Kingdom

⁎ Corresponding author. Tel.: +44 20 7594 7360; faxE-mail address: j.j.cillifers@imperial.ac.uk (J.J. Cillier

0301-7516/$ – see front matter © 2011 Elsevier B.V. Aldoi:10.1016/j.minpro.2011.05.002

a b s t r a c t

a r t i c l e i n f o

Article history:Received 6 August 2010Received in revised form 19 April 2011Accepted 8 May 2011Available online 14 May 2011

Keywords:Froth flotationProcess control

The last few decades have seen major advances in instrumentation and technology, and simplifications andmodifications of new flotation plant designs. This has allowed for significant developments in process control.In particular, the development of base level process control (control of pulp levels, air flowrates, reagentdosing, etc.) has seen significant progress. Long-term, automated advanced and optimising flotation controlstrategies have, however, been more difficult to implement. It is hoped that this will change as a result of thedevelopment of new technologies such asmachine vision and themeasurement of new control variables, suchas air recovery.This review looks at each of the four essential levels of process control (instrumentation, base level flotationcontrol, advanced flotation control and optimising flotation control) and examines current and future trendswithin each sub-level.

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© 2011 Elsevier B.V. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 582. Key variables and considerations in the control of flotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

2.1. Key variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592.2. Effects of plant layout and the location of the cell in the circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592.3. Types of process input disturbances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592.4. System constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

3. Instrumentation and base level flotation control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603.1. Pulp levels in cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

3.1.1. Instrumentation used for pulp level measurement and control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603.1.2. Base level control systems for pulp level control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

3.2. Air flowrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603.2.1. Instrumentation used for air flowrate measurement and control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603.2.2. Base level control systems for air flowrate control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

3.3. Slurry flowrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613.3.1. Instrumentation used for slurry flowrate measurement and control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613.3.2. Implementation of slurry flowrate in control systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

3.4. Elemental assaying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613.4.1. Instrumentation used for elemental analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613.4.2. Implementation of elemental assaying in control systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.5. Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623.5.1. Instrumentation used for density measurement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623.5.2. Implementation of density measurement in control systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.6. Reagent addition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623.6.1. Instrumentation used for reagent addition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623.6.2. Base level control systems for reagent addition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.7. Eh, pH and conductivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623.7.1. Instrumentation for the measurement of Eh, pH and conductivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623.7.2. Base level control systems for Eh, pH and conductivity control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

58 B.J. Shean, J.J. Cilliers / International Journal of Mineral Processing 100 (2011) 57–71

3.8. Gas dispersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633.8.1. Instrumentation used for the measurement of gas dispersion variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633.8.2. Implementation of gas dispersion variables in control systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

3.9. Machine vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633.9.1. Instrumentation and methods used for machine vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633.9.2. Implementation of machine vision in control systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

4. Advanced flotation control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644.1. Advanced control of mass pull and re-circulating load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.1.1. Mass pull control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644.1.2. Re-circulating load control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.2. Advanced control of grade and/or recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654.2.1. Model-based methods in advanced flotation control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654.2.2. Expert systems in advanced flotation control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

5. Optimising flotation control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665.1. Modelling-based methods in optimising flotation control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675.2. Expert methods in optimising flotation control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

6. Examples of approaches found in advanced/optimising flotation control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 687. Commercial advanced/optimising flotation control software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 688. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

1. Introduction

Froth flotation is one of the most broadly used separation methodsin the mineral processing industry. However, despite being intro-duced in the early 1900s and numerous years of research anddevelopment, flotation is still not fully understood and remainsrelatively inefficient. As such, large economic gains stand to be madethrough optimisation of many present processes (McKee, 1991;Hodouin et al., 2000; Moilanen and Remes, 2008).

It is important to realise from the outset that process controlconsists of several interconnected levels. Several authors, e.g. Roeschet al. (1976); McKee (1991); Laurila et al. (2002); and Gupta and Yan(2006), describe the process control of froth flotation by a hierarchy of3–4 inter-connected layers. The hierarchy described by Laurila et al.(2002) is presented in Fig. 1.

The lowest level is the instrumentation itself, which is the basis forall process control. As such, the choice/design and maintenance of theinstrumentation is of central importance to any process controlsystem. Furthermore, the correct choice of instrumentation can onlybe achieved if a detailed understanding of the functioning andapplication of the required instrument, within a given process, isacquired (Laurila et al., 2002).

Base level flotation control is focused on maintaining primaryvariables at setpoints. These primary variables include: pulp level, airflowrate and reagent addition rate. This is generally achieved throughthe usage of conventional SISO PID control; although more advanced

Fig. 1. Process control system level hierarchy fo

methods are now commonly used in modern control strategies.Similarly, traditional base level flotation control was applied to singlecells, although modern control strategies are now regularly applied toentire banks of cells (e.g. pulp level control).

The two higher tiers of flotation control are advanced flotationcontrol (AFC) and optimising flotation control (OFC). AFC involves therejection of the effects from input disturbances to the process (e.g. achange in feed grade) and maintaining performance parameters —

grade and recovery (although care should be taken when definingrecovery; in a dynamic situation accumulation of material within thesystem and lag times should be considered). OFC, on the other hand,aims to maximise overall financial profitability (commonly bymaximising grade and recovery). Both AFC and OFC attempt toachieve their objectives through manipulation of lower level controlsetpoints. It thus follows that the efficiency of AFC and OFC systemsare dependent on satisfactory lower level flotation control systemsbeing in place. Several cells are generally controlled simultaneously;and advanced control methods (which in control engineeringterminology classically refers to any control strategy more compli-cated than SISO PID control; and more recently to computer basedtechnologies) are used as PID control is insufficient.

Numerous years of research into the automation of froth flotationcontrol has been conducted to increase process efficiency, with effortsprior to the 1970s being largely unsuccessful. Several authors, such asMcKee (1991) and Laurila et al. (2002), agree that reasons for thisinclude: a lack of appropriate instrumentation and technologies, and

r flotation processes (Laurila et al., 2002).

59B.J. Shean, J.J. Cilliers / International Journal of Mineral Processing 100 (2011) 57–71

the old design of flotation plants; which consisted of numeroussmaller cells making process control more complicated and cumber-some. As such, operator intervention was the only method of processcontrol (and this is still largely true of many plants today).

However, the early 1970s saw extensive improvements inavailable instrumentation and the research into the development ofautomatic control of flotation began to show increasing promise(McKee, 1991; Thwaites, 2007). Subsequently, there has been majordevelopment in base level flotation control; although the develop-ment of robust, long-term, automatic A/OFC systems has proven morechallenging. According to McKee (1991) this is partially due to “theinherent complexity and unpredictability of the response of most flotationcircuits to upset conditions, unclear expectations of what a controlsystem can achieve, unrealistic objectives for control systems andexcessive complexity of the actual control strategies.” Osorio et al.(1999) included coupling among control loops, long varying lag times,and an “imperfect knowledge of the phenomenology of flotation and thelack of appropriate and precise instrumentation” as contributors forcomplicating control attempts.

Despite this, Laurila et al. (2002) believe that flotation is currentlyfacing “a new era in terms of automation and process control”, withthere being three main reasons for this:

• Henning et al. (1998), and Moilanen and Remes (2008) noted that“flotation circuit design is moving away from multiple recycle streamsand towards simpler circuits.” This simplifies regulation and controlof the over-all process.

• Authors such as Kallionen and Heiskanen (1993), and Carr et al.(2009) have noted that the sizes of flotation cells are increasing, themain benefits being: a reduction in capital expenditure andoperational costs, lower energy consumption per cubic metre, lessitems to maintain and a lower plant footprint. Fewer cells also meanless instrumentation is required; allowing for less-intricate processcontrol systems. However, this does mean more accurate instru-mentation is required. Fewer, larger cells also increase the incentivefor better base level control on each individual cell.

• Recent developments in instrumentation have seen the develop-ment of tools such as fieldbus technology and image analysis, andhave allowed for the assembly of “smart instruments”; devices thatuse self-diagnostics to provide information about the equipmentstatus and measurement quality.

Laurila et al. (2002) also highlight that as each flotation process isunique (e.g. cell configuration, instrumentation, ore, chemistry, etc.) alarge variety of A/OFC strategies have been developed and imple-mented, and a single, universal, control approach cannot be given.

The aim of this literature review is to broadly explore the variouscontrol strategies that have been/are being, developed and utilisedfrom a metallurgist's perspective. This work shall begin with a look atthe key variables and considerations in the control of flotation. Each ofthe four levels of flotation control will then be explored in detail.Lastly, a description of some available commercial control systemsand conclusions are presented. It should also be noted that althoughthis paper focuses primarily on cell flotation devices, as opposed tocolumn flotation devices, much of the literature and theory isapplicable to both.

2. Key variables and considerations in the control of flotation

Froth flotation is a three phase separation process for complex oresbased on the manipulation of the difference in hydrophobicity of thesolids. Suspended, hydrophobic metal-rich particles are contactedwith, and subsequently combine to, air bubbles — whilst the morehydrophilic gangue particles sink and are recovered to the tailsstream. The valuable-mineral loaded bubbles report to a froth phaseand overflow into a launder, before being recovered to theconcentrate stream. Although the process may sound relatively

simple other simultaneous sub-processes also occur. Examplesinclude: entrainment of gangue into the froth phase, coalescence ofbubbles, de-attachment of valuable particles from bubbles as theyimpact the froth phase, etc. (Ventura-Medina, 2000). The feedcomposition and upstream grinding stages prior to flotation alsosignificantly affect the process. Thus, in reality, the flotation process ishighly complex with Arbiter and Harris (1962) estimating that thereare approximately 100 variables that affect (to varying degrees) theflotation process. Moreover, co-interactions between variables furthercomplicate control efforts. For example, an increase in air flowratemay well result in a larger bubble size, which will subsequently affectthe bubble rise velocity, rate of attachment, gas holdup, froth depth,etc. — meaning other variables may be affected and need manipulat-ing after a given response time. As such, it is the high number ofvariables and the complex, non-linear, inter-relationships betweenthese variables that make flotation control in specific verychallenging.

2.1. Key variables

Laurila et al. (2002) suggest that the following variables –

specifically from an A/OFC viewpoint – are most important:

• slurry properties (density, solids content)• slurry flow rate (retention time)• electrochemical parameters/potentials (pH, Eh, conductivity)• chemical reagents and their addition rate (frothers, collectors,depressants, activators)

• pulp levels in cells• air flowrates into cells• froth properties (speed, bubble size distribution, froth stability)• particle properties (size distribution, shape, degree of mineralliberation)

• mineralogical composition of the ore• mineral concentrations in the feed, concentrate and tailings(recovery, grade)

• froth wash water rate (especially in flotation columns)

Manipulating/measuring each of these variables simultaneouslymay well be unnecessary to achieve a good process control result.However, each of these variables and their effects on the flotationprocess should be considered.

2.2. Effects of plant layout and the location of the cell in the circuit

The process layout is a key consideration in process control.Recycle streams in particular can make process control (e.g. pulp levelcontrol) more challenging, especially if accumulation and/or suddendischarges of material are allowed to occur. Additionally, differentsections of the plant require different process control regimes; therougher and scavenger sections being operated at comparatively lowfroth depths and high air flowrates, to achieve high mineral recovery;whilst cleaner sections operate with greater froth depths and lowerair flowrates in a bid to increase grade (Laurila et al., 2002).

2.3. Types of process input disturbances

It is important to identify the frequency and severity with whichinput disturbances can occur for a given flotation process. Wills andNapier-Munn (2006) suggest that if the grinding circuit controlsystems are running efficiently, variations in particle feed rate, pulpdensity and particle size should be minimal — with the flotationcircuit being responsible for compensating for variations in mineral-ogy and floatability of the ore.

60 B.J. Shean, J.J. Cilliers / International Journal of Mineral Processing 100 (2011) 57–71

2.4. System constraints

Existing circuit constraints need to be considered when imple-menting a process control regime. Two types of constraints exist.Firstly a process may be equipment constrained (e.g. a recoverycannot be achieved because the required air flowrate rate cannot besupplied). Secondly, a process may be constrained by another part ofthe system; an example being the limitation of the maximumrecovery of a desired species, at a high concentrate grade objective,within a flotation plant as a result of insufficient liberation by thegrinding circuit (McKee, 1991). Bergh and Yianatos (2011) add that“in practise the plant operating point that satisfies the overall economicgoals of the process will lie close to the intersection of constraints.”

3. Instrumentation and base level flotation control

Information about the input disturbances, process operatingparameters and final product quality is required before optimisationand control can be performed; with the quality of measuredinformation largely determining the efficiency of an implementedcontrol system. However, despite the availability of instrumentationfor the measurement of important parameters such as: ore compo-sition, flowrates and less ore specific properties (e.g. pulp levels,density, pH) — essential properties such as liberation degree, surfacechemistry, bubble size distribution, bubble loading, etc. remaindifficult to measure and infer (Bergh and Yianatos, 2011).

Currently, most existing instrumentation on flotation plantsmakesuse of analogue signal technology; with signals requiring conversioninto a digital format before interfacing with automation systems. Assuch, it is thought that analogue technology is to be replaced by digitalfieldbus technology. This will result in completely digital communi-cation between instrumentation and base level control systems atsource, and has already enabled the decentralisation of pulp level andair flowrate base level control systems. This has also allowed for betterintegration of base level control during flotation cell design (Laurilaet al., 2002). Continued development of base level control is nowlargely focused “towards operational aspects that facilitate and speed upthe setting of control loops” (Moilanen and Remes, 2008).

3.1. Pulp levels in cells

3.1.1. Instrumentation used for pulp level measurement and controlLaurila et al. (2002) and Carr et al. (2009) suggest that the most

typical methods of pulp level measurement are:

• Float with a target plate and ultrasonic transmitter• Float with angle arms and capacitive angle transmitter• Reflex radar

Other methods mentioned in literature include:

• Hydrostatic pressure measurement. Authors such as Roesch et al.(1976); Hamilton and Guy (2000); and Maldonado et al. (2008)describe methods whereby the hydrostatic pressure is measured todetermine the pulp level. Accurate measurement requires that boththe slurry density and air holdup in the pulp be known.

• Microwave radar and ultrasonic transmitters. Microwave or ultra-sonic beams are emitted towards the froth and are reflected at theslurry surface before being measured, and the pulp level beinginferred (Hamilton and Guy, 2000).

• Conductivity and capacitance Hamilton and Guy (2000) describe twotechniques whereby the large difference in dielectric constantbetween gasses and liquids is used to determine the pulp level.Similarly, the difference in electric conductivity, measured using aconductivity probe, can also be used to locate the froth–pulpinterface (Wills and Napier-Munn, 2006; Maldonado et al., 2008).

Laurila et al. (2002) add that accurate level measurement is oftenproblematic as the slurry–froth transition is not sharp and variationsin slurry density and/or very dense froth layers often exist. Thisespecially complicates methods using direct ultrasonic or hydrostaticpressure measurements.

Lastly, Carr et al. (2009) highlight that “control valve options arelimited due to the eroding conditions and large variations in flowrate.”Dart and pinch valves are generally used (neither being optimal), andboth being occasionally incorrectly sized. The optimal operating rangeof these valves is 30–60% open, although these valves are commonlyseen operating at below 30% open. Operating a valve near the fullyclosed position causes increased wear, while operating near the fullyopen position reduces the control range of the valve.

3.1.2. Base level control systems for pulp level controlStenlund and Medvedev (2000); Kämpjärvi and Jämsä-Jounela

(2003); and Carr et al. (2009) report thatflotation cells traditionally usefeedback PI control to ensure pulp levels remain at desired set-points;althoughWills and Napier-Munn (2006) add that feedforward control isalso regularly integrated to account forflowrate variationsupstream. Thisis achieved bymanipulating the tailings flow from the cell by adjustmentof the slurry outlet valve. This technique is effective for the control ofisolatedcells, but is also commonlyused to control abankof cells in series.This approach is problematic, as each individual cell control loopattemptsto independently maintain the pulp level at the set-point. As such, acontrol action for one cell is a disturbance for the next; the net resultbeing that each cell drives the following cell off its set-point.

As such, sophisticatedmultivariable model-based control methodshave been developed; whereby the whole bank of cells is modelledand compensations between adjacent cells calculated and/or consid-ered. Two examples of multivariable control methods are presented inStenlund and Medvedev (2000). The first makes use of a ‘decouplingcontroller’ model, where ‘compensator’ parameters are introduced(representing the dependencies of each cell in a series on prior cells)to counteract the interactions between cells. The second methodintroduced is a multivariable model-based feedback controller, whichmanipulates flows out of each cell simultaneously so as to continuallyminimise a defined error function. Kämpjärvi and Jämsä-Jounela(2003) described an alternative multivariable model whereby a feed-forward controller was linked with traditional PI controls on each cell.Moilanen and Remes (2008) described a similar feed forward controlalgorithm. Despite this, Carr et al. (2009) reports that these “moresophisticated methods… are rarely used in industrial processing plants.”Commercial control packages such as FloatStar Level Stabiliser byMintek, or CellStation by Outotec, also aim to control all cell levelssimultaneously using advanced control techniques. Both havereportedly been trialled successfully and installed on industrial plants(www.mintek.co.za; www.outotec.com).

FromanA/OFCperspectivepulp level control is important as it dictatesthe froth depth (defined as the distance from the pulp/froth interface tothe overflow lip). Theoretically, a deeper froth allows for increaseddrainage of mechanically entrained gangue, and subsequently, a higherconcentrate streamgrade (Wills andNapier-Munn, 2006). As such, A/OFCsystems commonly manipulate the setpoints of pulp-level controllers.

3.2. Air flowrates

3.2.1. Instrumentation used for air flowrate measurement and controlLaurila et al. (2002) report that there are three common methods

of measuring air flowrates in flotation processes. Two of the methodsmake use of differential pressure metres, which “are popular inindustry, including flotation, due to their low price, simple principle andfairly low requirement of maintenance.” The three methods are:

• Thermal gas mass flow sensor. The cooling effect of the air as it flowspast a sensor is measured and correlated to an air flowrate. These

61B.J. Shean, J.J. Cilliers / International Journal of Mineral Processing 100 (2011) 57–71

instruments are unobtrusive to the air flow, although they areexpensive and factory calibrated, making changes difficult.

• Differential pressure metre with venturi tube. Green and Perry (2007)describe that the narrowed venturi tube acts as a restriction to theflow, resulting in a pressure drop that is measured and a flow ratedetermined. This method is reliable, produces tolerable pressuredrops and is accurate, although the venturi tube has large spacerequirements and is expensive Laurila et al. (2002) add that “anorifice plate is not a suitable solution due to the significant pressure lossit causes”.

• Differential pressure transmitter with Pitot tube or annubar tube. Boththe Pitot tube and annubar element determine the gas flowrate, in apipe, by comparison of the internal pipe pressure and the static gaspressure. The difference between the methods is that the Pitot tubeonly has one measurement point, while the annubar element hasseveral measurement points and thus provides an average airvelocity. Both methods are deemed accurate and the observedpressure drop is small. Fig. 2 illustrates the instrumentation used forair flowrate measurement.

Problems associated with differential pressure metres includelarge space requirements, with large sections of straight piping beingneeded to ensure a fully developed flow profile. One solution is todecrease the pipe size, as the required straight pipe length is related topipe diameter. Butterfly valves are used to control the air flow as theyare cheap and sufficient for the task (Laurila et al., 2002).

3.2.2. Base level control systems for air flowrate controlWills and Napier-Munn (2006) elaborate just how important and

useful aeration control is; adding that “flotation generally respondsfaster to changes in aeration, than to changes in froth depth, andbecause of this aeration is often a more effective control variable”.Similarly, they infer (by use of an example) that in comparison toreagent addition, air is by far the “cheaper ‘reagent’ and leaves noresidual concentration if used in excess.” As such, air flowrate isfrequently incorporated into A/OFC systems, often in conjunctionwith pulp level and/or reagent addition control systems; an examplebeing the simultaneous manipulation of air-flow rate and pulp levelto control mass pull.

Carr et al. (2009) note that “the control of flotation aeration is easierthan slurry level control”. Luyben and Luyben (1997), Laurila et al.(2002), and Carr et al. (2009) agree that a simple, well-tuned,feedback/feedforward PI/PID control loop is adequate to accuratelyregulate air flow bymanipulation of the control valve setting. Sizing ofthe control valve is of central importance for effective control.Oversized valves are often fitted to infer a smaller pressure drop,but in reality result in a limited control response (Luyben and Luyben,1997); and can significantly, and rapidly, affect flotation performanceand pulp level control. Smith et al. (2008), suggest that down-the-

Fig. 2. Illustration of (A) thermal gas mass flowmeter, (B) ventu

bank air flowrate profile control is also advantageous. If the cell airflowrates are not controlled individually, and air is fed to a group ofcells, butterfly valves are often operated manually to adjust the flowto each cell. Lastly, Laurila et al. (2002) add that “flotation cells withself-aspirating aeration mechanisms often do not have automatic airflowcontrol. The available range of airflow control is anyhow limited. Thisproblem is pronounced at high altitude.” This limits the potential for theimplementation of advantageous A/OFC strategies.

3.3. Slurry flowrates

3.3.1. Instrumentation used for slurry flowrate measurement and controlMagnetic flow metres are commonly used to measure slurry

flowrates and are based on Faraday's principle of induction, with thedevice consisting of an electromagnet coiled around an insulatedlength of pipe. Electrodes are installed at opposite sides of the pipe,which enable an electric current to be generated through the flowingfluid and measuring device. From this measured current a flowratecan be determined. This method is non-obtrusive and modernmagnetic flowmeters take up to 30 measurements per minute. Slurrymeasurement is problematic, however, as solids and air bubblesdecrease performance. Moreover, if magnetic solids (e.g. magnetite)are present de-magnetisation is required (Laurila et al., 2002). Slurryflowrates can also be controlled by variable or fixed speed pumps thatare capable of handling slurries. Suitable valve options have alreadybeen discussed in Section 3.1.1.

3.3.2. Implementation of slurry flowrate in control systemsIn flotation circuits, slurry flowrates are generally manipulated to

control pulp levels in cells (see Section 3.1.2), and are not controlledto a setpoint. Nevertheless, the measurement of slurry flowrates areimportant, as Laurila et al. (2002) point out, for A/OFC, and allow forcalculating re-circulating loads and performing mass balance calcu-lations. Slurry flowrate measurement is also important for reagentaddition base level flotation control.

3.4. Elemental assaying

3.4.1. Instrumentation used for elemental analysisOn-line X-ray fluorescence (XRF) analysers provide elemental

assays from process flow streams and are now considered standardhardware on large scale flotation plants (Garrido et al., 2008). Severalpoints of the process can be sampled, with some modern XRFanalysers handling up to 24 streams andmostmachines being capableof analysing for several elements and solids content. The time toanalyse a single sample can range from 15 s to a minute, and thesampling cycle time is between 10 and 20 min — depending on thenumber of sample points attached to the analyser (Laurila et al., 2002;

ri tube with differential pressure metre and (C) Pitot tube.

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Bergh and Yianatos, 2011). Accuracy ranges from 1 to 6% anddetection limits are as low as 3–30 ppm (Moilanen and Remes,2008). Moreover, Haavisto et al. (2008) introduce a new method ofanalysis; “the visual and near-infrared reflectance spectroscopic analysisof process slurries”. This measurement aims to be “a supplementarymethod, which complements the on-line assay information availablefrom an XRF analyser.” It is further reported that “spectral informationcan be used to accurately predict element contents in the slurry inbetween successive XRF analyses”, and as these “measurements can betakenwith high frequency as opposed to sparse XRF analysis, a practicallycontinuous on-line estimate of the slurry contents is reached.” Thiswould allow for any process disruptions to be rapidly identified.

Despite the obvious benefits of online XRF analysis Garrido et al.(2008) report that these analysers are generally “under-utilisedbecause operators do not trust the online information given by estimationmodels”. They further describe a calibration method for estimationmodels that “minimise the effects of the uncontrollable disturbancesduring the estimation.”

3.4.2. Implementation of elemental assaying in control systemsWills and Napier-Munn (2006) report that “the key to effective

(flotation) control is online chemical analysis, which produces real-timeanalysis of the metal composition of process streams”. However, therelatively long sampling cycle times mean that input disturbances ofhigh frequency can be missed, making it difficult to capture theexperimental data required to form dynamic models of the process(Bergh and Yianatos, 2011); and take required control actions. Moreover,Remes et al. (2007) conducted a study into the effect of speed andaccuracy of on-line elemental analysis on flotation control performance.The study concluded that an increase in sampling cycle time results in astrong decline in controllability, resulting in negative economic impacts.

3.5. Density

3.5.1. Instrumentation used for density measurementRoesch et al. (1976) report that nuclear density metres are

commonly used for density measurement on flotation plants.Gamma radiation is emitted by a radioactive isotope and theattenuation of the radiation by the slurry is measured, from whichthe density can be determined from a prior calibration. This method isnon-obtrusive to the process flow. This is in agreement with Laurilaet al. (2002), who add that air bubbles in the slurry oftenmake use of anuclear density metre impossible and choice of location in the processis an important consideration. Additionally, some on-stream XRFanalysers are now capable of density measurement.

3.5.2. Implementation of density measurement in control systemsDensity measurements are used in mass balance calculations,

which are associated with A/OFC (Laurila et al., 2002). Furthermore,control/manipulation is normally preformed in the grinding circuit/s(Wills and Napier-Munn, 2006).

3.6. Reagent addition

3.6.1. Instrumentation used for reagent additionA variety of alternative equipment for maintaining/setting reagent

addition rates are used industrially. Laurila et al. (2002) suggest themain reasons for this are due to the seemingly negligible amounts thatneed to be added (often measured in millimetres per minute) and thelarge variety of different reagents added, each with their ownchemical properties and attributes. Common methods include:

• A simple on-off type dosing systemwhich periodically opens a valveand allows reagent to enter the process. Regular checks are requiredto ensure the correct amount is added, as this method can be veryinaccurate.

• Metering pumps are also used, especially if volumes to be added arevery small or cost is of importance. This method is more accurate,but the pumps are costly and require regular maintenance.

3.6.2. Base level control systems for reagent additionBase level reagent addition control commonly consists of a

feedforward ratio-type control, as mentioned by Hodouin et al.(2000), whereby the reagent addition rate is altered according to thefeed rate of ore to achieve a reagent (in slurry) concentration setpoint(e.g. grammes of reagent per ton of ore). This reagent concentrationsetpoint may then be altered retrospectively by operators in afeedback fashion after considering composition results from an XRFanalyser. Similarly, the setpoint could be manipulated by an A/OFCsystem to achieve a required metallurgical result; although Wills andNapier-Munn (2006) add that this is more common with collectorcontrol, with frother addition rate setpoints usually being setmanually.

3.7. Eh, pH and conductivity

3.7.1. Instrumentation for the measurement of Eh, pH and conductivityMeasurement of electrochemical potential (Eh), pH and conduc-

tivity provide information on the surface chemistry of the particles inthe slurry and are the only direct, non-intrusive methods ofdetermining what is occurring chemically within the flotation cell(Ruonala, 1995). Laurila et al. (2002) report that pH is a commonlymeasured electrochemical property, and is related logarithmically tothe hydronium ion activity in solution. Measurement is achieved byusing ion selective electrodes, although this is often problematic as“the electrodes are easily contaminated by active substances in theslurry.” As such, sampling systems are often used for pH measure-ment, where washing of the electrodes and regular maintenance canbe performed.

Conductivity measurement can often be used instead of, or inconjunctionwith, pHmeasurement— as both provide complimentary/similar information. Conductivity metres are generally cheaper andare more suitable for highly alkaline solutions, although their useshould be avoided in highly aerated systems. In addition, Wang andCilliers (1999), and Bennett et al. (2002) performed studies whereconductivity measurements were used to determine froth density andflow regimes.

Eh measurement too is problematic, with Woods (2003) addingthat “maintaining electrode probes so that they respond appropriately inthese plants is difficult to achieve”, and concluding that the choice ofmaterials in Eh probes requires further research.

3.7.2. Base level control systems for Eh, pH and conductivity controlBase level control of pH involves maintaining the slurry pH at a

desired setpoint, through manipulation of acid or lime addition rates,with PID control loops being adequate for the task (Laurila et al.,2002). Long response times of the system mean a lag time shouldfollow any corrective action/s performed to fully appreciate the effectsit has had (Wills and Napier-Munn, 2006). Commercial controllers,such as FloatStar pH Controller, are also available and use advancedcontrol methods.

Control of Eh usually involves the addition of nitrogen or air to thesystem to alter the electrochemical potential (Woods, 2003). Muchresearch into improving metallurgical results through Eh manipula-tion (A/OFC) has been published over the last four decades. Ruonalaet al. (1997); and Woods (2003) present thorough reviews of thiswork on both laboratory and industrial scales, for several ore types. Asummary of a brief literature review, listing the ore-type the studieswere conducted on, is listed below in Table 1.

Table 1Summary of literature review for Eh measurement/control in froth flotation.

Author and date Mineral type

Berglund (1991) Pyrite, sphalerite-galena, chalcopyriteBruckard et al. (2007) Arsenopyrite, lollingnite and arsenic from

tin bearing oresChander and Fuerstenau (1975) Copper and chalcociteClark et al. (2000) Chalcocite, chalcopyrite and borniteGuo and Yen (2005) Enargite and chalcopyriteHayes and Ralston (1988) Galena, chalcopyrite and sphaleriteHicyilmaz et al. (2004) PyriteHintikka and Leppinen (1995) Complex sulphide ores and gold bearing oresKirjavainen et al. (1992) Copper ores with rich copper–zinc–lead pyrite oresKocabağ and Güler (2007) Pyrite, galena, chalcopyriteLeppinen et al. (1997) Copper and zinc rich complex oresQing et al. (2008) Lead–silver–zinc complex oresRoos et al. (1990) Chalcocite and covelliteRoos et al. (1990) Copper and chalcopyriteShen et al. (1997) Sphalerite and pyriteUribe-Salas et al. (2000) Galena, chalcopyrite and pyritic oreWalker et al. (1984) ChalcociteYuan et al. (1996) Complex copper/zinc sulphide ore with

pyrite and pyrrhotite

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3.8. Gas dispersion

3.8.1. Instrumentation used for the measurement of gas dispersionvariables

Gomez and Finch (2007) report that “gas dispersion is the collectiveterm for superficial gas (air) velocity (volumetric air flowrate per unitcross sectional area of cell, Jg), gas holdup (volumetric fraction of gas in agas-slurry mix, εg) and bubble size distribution (Db)”. The publicationalso details the equipment used for the measurement of thesevariables.

3.8.1.1. Superficial gas velocity measurement sensor. The sensor consistsof a vertically positioned tube; the lower end is partially submerged inthe pulp zone to collect bubbles (see Fig. 3, part A). The continuousversion has an orifice valve mounted on the air outlet. When air isallowed out of the orifice valve, time is given for a pressure steady-state to be reached andmeasured (i.e. the rate of air into the column isequal to that out the orifice valve). The volumetric air flowrate isthen inferred from a previous calibration and Jg calculated. Current

Fig. 3. Schematics of (A) gas velocity, (B) gas holdup and (C) bu

problemswith the design include the requirement of a range of orificevalves to suit all gas velocities and the build up of froth within thesystem (Torrealba-Vargas and Finch, 2006; Gomez and Finch, 2007).

3.8.1.2. Gas holdup measurement sensor. “The sensor is based onMaxwell's model that relates the concentration of a non-conductingdispersed phase to the conductivities of the continuous phase and thedispersion.” This requires two measurements in separate vessels (seeFig. 3, part B); one vesselmeasures the conductivity of the aerated pulp;the othermeasures the conductivity of the air free pulp (achieved usinga syphon). The ratio of these measurements is used to solve Maxwell'smodel and estimate the gas holdup. This method ensures continuousmeasurement; although care is required when choosing the openingsizes of the syphon to ensure no bubbles enter the vessel and result ininaccuracies (Gomez et al., 2003; Gomez and Finch, 2007).

3.8.1.3. Bubble size measurement sensor. The McGill bubble sizingdevice (see Fig. 3, part C) is able to measure the full bubble sizedistribution found in the pulp phase. A sample is drawn from the pulpvia a tube and directed into a sloped viewing chamber (the slopedwindow allows for a near mono-layer of bubbles to form) exposed to apre-set light source. The continuous flow of bubbles is then capturedvia image analysis. The accuracy of the measurement is difficult toestablish, although themethod is widely used and continues to evolveby improvements dictated by field trials (Gomez and Finch, 2007).

3.8.2. Implementation of gas dispersion variables in control systemsA recent publication by Bartolacci et al. (2008) focused on using

gas dispersion sensors in conjunction with a Froth Stability Columnand machine vision (both still to be discussed). Air flowrate, pulplevel, reagent dosage and feed rate were varied whilst dispersionparameters, froth stability, bubble surface area flux and metallurgicalresults were measured. The results indicated a high dependence ofgrade and recovery on dispersion parameters and froth stability; aclear indication of the potential of these measurements beingintegrated into an A/OFC system.

3.9. Machine vision

3.9.1. Instrumentation and methods used for machine visionMachine vision makes use of cameras positioned above flotation

cells to record digital images of the froth surface. Several froth featurescan be extracted from these images and used for control purposes.

bble size measurement devices (Gomez and Finch, 2007).

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These features are categorised into three types; namely: physical,statistical and dynamic properties. Within each category severalmethods exist for extracting different variables and/or features. Forexample bubble size, a physical property of a froth, is one feature thatmay be extracted through usage of bubble edge or watershedalgorithms. An extensive amount of literature has been writtenabout the methods by which features are extracted from froth; with afull, recent, literature review being carried out by Aldrich et al. (2010)(see Table 2).

Aldrich et al. (2010) highlight that the use of physical featuresfor control purposes remains problematic. Issues associated withphysical feature extraction include:

• It is commonly observed that the surface bubbles of flowing frothsare significantly larger than those in the layers immediately below;the lower layers forming the predominant portion of the volumeoverflowing into the launder. This cannot be corrected for readily.

• Neethling et al. (2003) observed that the surface film sizedistribution is not necessarily representative of the bubble sizedistribution in the underlying froth layer Wang and Neethling(2009), however, have determined a method to relate surface filmsize to underlying bubble size distribution.

• The watershed method often over-segments larger bubbles andunder-segments smaller bubble sizes.

Mobility feature extraction has been shown to be particularlyuseful within the context of A/OFC.

3.9.2. Implementation of machine vision in control systemsIt is common in industry for operators to control and regulate a

flotation plant by visual inspection of the froth surface. Indeed, anunderstanding of processes occurring within the froth phase is centralto understanding the overall behaviour of flotation systems (Glembotskii,1972; Cutting et al., 1986;McKee, 1991;Mathe et al., 1998). The structureof froths has a significant effect on grade and recovery; with severalpublications (e.g. Supomo et al., 2008; Moilanen and Remes, 2008)suggesting a link between froth velocity and metallurgical performance.As such, the development and implementation of machine vision was anattempt to refineandautomate the control basedon the froth appearance.Currently, numerous commercial machine vision systems are available;making the on-line measurement of froth velocity possible. This is usefulin the implementation of A/OFC, an example being themeasurement andcontrol of mass pull. Moreover, the faster dynamics of machine vision(1 min) compared to XRF technology (10–20 min), open the possibility

Table 2Overview of methods used for feature extraction by machine vision (Aldrich et al., 2010).

Type Methods Froth variables or features R

Physical Edge Bubble size and shape B(

Watershed Bubble size and shape FC

Statistical Spectral RGB BaV

FFT MWavelets BTexture H

Co-occurrence matrices Spatial and neighbouring grey level AFractals Fractal descriptor BLatent variables PCA B

Neural networks EaN

Dynamic Mobility Bubble tracking BBlock matching B

MPixel tracing N

Stability B

for building better predictive flotation A/OFC models, based on visualaspects of the froth as opposed to stream grades (Bergh and Yianatos,2011).

4. Advanced flotation control

AFC, also known as stabilising control, aims to reject the effects ofinput disturbances (e.g. a change of ore type) and maintain theflotation process as close to steady state as possible (McKee, 1991;Laurila et al., 2002). This is generally achieved by controlling masspull; recycle load; stream grade and/or recovery to setpoints (usuallyset by operators or an optimising controller), with the manipulatedvariables being the setpoints of base level controllers (i.e. pulp level,air flowrate, reagent addition rate and pH/conductivity). As such,effective AFC is only possible if robust, efficient base level flotationcontrol systems are in place.

4.1. Advanced control of mass pull and re-circulating load

An effective method of keeping the plant's mass balance at steadystate is to maintain constant mass pulls and/or re-circulating loads atsetpoints (often in conjunctionwithmaintaining grade and/or recovery).

4.1.1. Mass pull controlMass pull refers to the amount of concentrate collected. A recent

paper by Supomo et al. (2008) describes the successful installationand operation of VisioFroth, a commercial machine vision basedcontrol system developed by Metso, at PT Freeport in Indonesia. Thecontrol system measures the velocity of the overflowing froth, andthen adjusts the froth-depth to achieve the desired mass pull. Analternative commercial package, FloatStar Flow Optimiser by Mintek,uses density and flowrate measurements to calculate mass pull. Therequired mass pull is then obtained through manipulation of pulplevel and air flowrate (www.mintek.co.za).

4.1.2. Re-circulating load controlRe-circulatingmaterial through a flotation plant results in decreased

residence time of material per flotation cell, but does allow for thevaluable material to spend more time in the flotation plant as a whole;and, subsequently, increases the overall recovery (Wills and Napier-Munn, 2006). However, this re-circulating loadmayneed to be varied toaccount for input disturbances (McKee, 1991). FloatStar Flow Optimiseris an example of a commercial control package which uses the

eferences

anford et al. (1998); Forbes and De Jager (2004b); Forbes et al. (2006); Lin et al.2007a,b); Wang et al. (2003); Wang and Stephansson (1999)orbes and De Jager (2004a); Sadr-Kazemi and Cilliers (1997); Ventura-Medina andilliers (2000); Yang et al. (2008)onifazi et al. (2005a,b); Gebhardt et al. (1993); Hargrave et al. (1996, 1998); Hargravend Hall (1997); Morar et al. (2005); Oestreich et al. (1995); Siren (1999);athavooran et al. (2006)oolman et al. (1994)artolacci et al. (2006); Liu and MacGregor (2007, 2008)argrave and Hall (1997); Holtham and Nguyen (2002)ldrich et al. (1995, 1997); Bezuidenhout et al. (1997); Moolman et al. (1994, 1995a,b)onifazi et al. (2000); Hargrave and Hall (1997); Hargrave et al. (1998)artolacci et al. (2006); Liu et al., (2005); Liu and MacGregor (2007, 2008)strada-Ruiz and Perez-Garibay (2009); Hyötyniemi and Ylinen (2000); Jeanmeurend Zimmerman (1998); Kaartinen and Hyötyniemi (2005); Moolman et al. (1995c);iemi et al. (1997)otha (1999)arbian et al. (2007); Forbes and de Jager (2007); Holtham and Nguyen (2002);oolman et al. (1994); Supomo et al. (2008)guyen and Holtham (1997)arbian et al. (2003, 2005, 2006); Moolman (1995); Morar et al. (2006)

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measurement and manipulation of mass pull rates to regulate theamount of material recycled (www.mintek.co.za).

4.2. Advanced control of grade and/or recovery

AFC commonly refers to strategies that aim to maintain gradeand/or recovery (Laurila et al., 2002). However, McKee (1991) statesthat “A [stabilising] control system which is capable of firstly stabilisingcircuit performance, and then driving the circuit to a desired grade orrecovery operating point, would undoubtedly be considered highlysuccessful.” — highlighting the importance of the stabilising aspect ofAFC.

The review of the successfulness of alternative AFC strategies ismade difficult, as follow-up reports of implemented control strategiesare uncommon, as noted by McKee (1991); who suggested that thiswas because most control systems do not remain in operation forperiods of years after installation and are shut down. Furthermore,Wills and Napier-Munn (2006) add that few (if any) plants can claimto have fully automated control systems that can run the plant un-supervised for long periods of time. Thus it seems that despite thelarge body of literature devoted to the subject, the successfulapplication of AFC (and indeed OFC) techniques into industrialapplication have been largely unsuccessful. Reasons given for this inliterature include:

• Design of control systems are insufficiently thought out during plantdesign stages (Narraway et al., 1991; Thwaites, 2007; Bergh andYianatos, 2011); and major control variables are often onlyidentified once the plant is operational (Wills and Napier-Munn,2006). This commonly results in poorly designed, incompetent,control systems being installed on new plants; with later (ofteninadequate) modifications being required; or additional ad-hoccontrol systems being added on as ‘after-thoughts’.

• A vast knowledge of control systems and jargon is required todevelop, install and maintain A/OFC systems; with the majority ofoperators/metallurgists/management not having a background incontrol engineering (Hodouin et al., 2001; Thwaites, 2007; Willsand Napier-Munn, 2006).

• As already discussed, the non-linear, complex behaviour of flotationsystems complicates modelling attempts; making the design ofrobust, effective controllers – that can deal with large ranges ofoperating conditions – difficult.

It is partially for these reasons (specifically the latter two) thatWills and Napier-Munn (2006) argue that the best control systemsare those that interact with the operator (i.e. supervisory controlsystems), giving explanations, when alterations to setpoints/variablesof base level flotation control systems are required; and as such,experienced, conscientious operators currently remain a competitivealternative towards any automated control system.

According to Gupta and Yan (2006), there are broadly two types ofAFC used in mineral processing (a hybrid between the two is alsopossible). These are:

• Use of model-based methods• Use of expert control systems

The evolution and current trends of each of these branches willnow be discussed.

4.2.1. Model-based methods in advanced flotation controlModel-based methods can be further sub-categorised into two

categories, namely: empirical and phenomenological modelling (Polatand Chander, 2000).

Empirical models make use of statistical methods to relatemeasured input and output plant data, such that multivariable modelsrelating between two or more independent and dependant variablescan be established and used for predictive control (e.g. controlling

collector addition rate based on concentrate grade). Furthermore,continual analysis of plant data and corrective adjustment of thepredictive (model-based) controller make it adaptive to changingconditions (i.e. adaptive control). Adaptive control is especiallyimportant within the context of flotation control, which is prone tonon-linear, complex behaviour. As such, many predictive flotationcontrol systems often (but not always) include adaptive controlaspects. By 1991, Thornton noted that although the amount ofliterature devoted to multi-variable model-based control was exten-sive, the number of applications in industry was still comparativelysmall; with McKee (1991) noting that 5 control strategies wereparticularly common at the time (and largely remain so). These are:

• “Feed back control of collector addition to maintain recovery set points.• Feed forward control of collector based on the calculated metal contentof the new feed.

• Maintaining concentrate flows within limits, usually by varyingaeration rates or pulp levels.

• Maintaining circulating loads within limits, again by variation ofaeration rates or pulp levels.

• Controlling aeration rates or pulp level to obtain concentrate grade setpoints.”

Despite the apparent popularity of adaptive multivariable model-based controllers, Desbiens et al. (1994), and Gupta and Yan (2006)note issues with the stability of adaptive control; with the controllersbecoming saturated, and un-adaptive, after a period of time.Moreover, in spite of the majority of the predictive multivariablecontrol strategies being based on empirical correlations, research anddevelopment of phenomenological models – whereby relationshipsbetween cause and effect are devised through an understanding of thephysics of the flotation process – for use in predictive controllers wasalso conducted. As such, phenomenological modelling methods canbroadly be classified into 3 groups; namely: kinetics, populationbalance and probabilistic based modelling (Polat and Chander, 2000).The success of phenomenological modelling, within an AFC context, isdebatable.

The use of first-order flotation kinetics modelling is thoroughlycovered by Polat and Chander (2000), and has undoubtedly receivedthe most attention in literature. Kinetics modelling “is based onthe assumptions that the rate of the particle–bubble collision process isfirst-order with respect to the number of particles and that thebubble concentration remains constant”. Numerous batch experiments(Imaizumi and Inoue, 1963; Tomlinson and Flemming, 1963; Harrisand Chakravarti, 1970; Jameson et al., 1977; Dowling et al., 1985;Rastogi and Aplan, 1985) and continuous flotation tests (Jowett andSafvi, 1960) support the use of the first-order rate equation. This hasallowed a flotation cell to be modelled using the chemical reactoranalogy; whereby the removal of solids from the pulp phase is definedby a first order rate equation. This means a bank of cells can beapproximated by perfectly mixed CSTR's in series (Gaudin, 1957;Niemi and Paakkinen, 1969; Atkins et al., 1986; Yianatos andHendrìquez, 2006). Subsequently, efforts have been made toaccurately determine the so called ‘over-all flotation rate constant’(or k). This is not trivial, as k is dependent on particle size, degree ofliberation, air flowrate, agitation, etc. To account for this, variouscontinuous distribution functions of k have been devised (Polat andChander, 2000); although previous reviews by Dowling et al. (1985)conclude that no single distribution model could sufficiently repre-sent k; and Roesch et al. (1976), suggest that attempts based on thefirst order kinetics assumption are “approximative and hide secondarydetails”.

Alternatively, a population balance model is presented by Bascar(1982), Bascar and Herbst (1982), and Bascur (2000). The three phasemodel represents each mineralogical species and particle size, witheach particle species being classed according to state in the slurry (i.e.free in pulp, attached to bubble in pulp, free in the froth, attached to

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bubble in froth). Kinetic equations relate transfer of particles betweenslurry states and hydrodynamic considerations (e.g. power dissipa-tion into pulp, gas holdup in pulp, etc.) are incorporated into thepopulation balance. This makes it possible to simulate the effects ofmanipulated variables such as air flowrate, pulp level, agitation, etc.on a flotation process.

Despite much research into both empirical and phenomenologicalbased modelling, authors such as Bergh and Yianatos (2011) andMcKee (1991) highlight several issues that still exist; the latterconcluding that “Multivariate predictive control is ideally the solutionfor high quality control. However, to be applicable without losing itsbenefits, good measurements, acceptable regulatory control of localobjectives (i.e. base level controls), reliable dynamic models, explicitlystated process constraints and new methods to promote robustness areneeded. Flotation processes have weaknesses in most of those aspects.” Itis for reasons such as these that expert control systems (wheredecision making by operators is automated by use of artificialintelligence) are used.

4.2.2. Expert systems in advanced flotation controlThe potential of expert systems in the mineral industry was

recognised as early as 1983 (Bearman and Milne, 1992); and wereintroduced into A/OFC as modelling of flotation systems is difficult,they are suitable for the handling of non-linear systems, and theyautomate (and standardise) decision making by operators. Althoughseveral methods of Artificial Intelligence (AI) based control systemsexist, three important techniques include:

• Artificial neural networks (ANN) — data-driven computing devices,comprising of a large number of neurons, inter-connected by a pre-determined network of synapses on a large scale. These neurons arearranged in several layers and adjustable numerical weights areassociated with the connecting synapse network (see Fig. 3). Neuralnetworks are trained by iteratively updating the associated weightmatrix, such that a set of outputs can be predicted for a given set ofinputs. As such, an ANN based controller can be ‘taught’ how tomanipulate base level control system's setpoints to maintain a givenmetallurgical objective; an example being the changing of collectoraddition rate to maintain recovery (Aldrich et al., 1997; Gupta andYan, 2006) (Fig. 4).

• Inductive machine learning — makes use of mathematical models togenerate rules and form induced decision trees, and is based on theconcept of information entropy. A set of samples, each with acorresponding vector of classifying attributes (e.g. bubble size, frothvelocity, etc.) is assessed. The vector is then split according to themost informative attribute (each split forming a branch of thedecision tree); with each newly formed subset subsequently beingre-split (according to a different attribute) until each subset consists

Fig. 4. Simplified diagram of a neural network (Gupta and Yan, 2006).

of examples of a specific class. As such, a given system (e.g. a frothsurface) can be categorised— and appropriate control action taken ifrequired (Aldrich et al., 1997; Filipic and Junkar, 2000).

• Fuzzy logic — reasoning that serves to be approximate rather thanprecise. Compared with binary logic, where a result is false or true(or quantitatively 0 or 1), fuzzy logic caters for a degree of truth(anywhere between 0 and 1). Flotation system parameters (e.g.pulp level) are divided into fuzzy sets (according to definedmembership functions), which can subsequently be combined toform fuzzy subsets. A response to a fuzzy set/subset is then applied,using IF–THEN rule based strategies (Gupta and Yan, 2006). Forexample, if froth velocity and air rate are the variables beingarranged into fuzzy subsets, the control logic might be somethinglike: IF froth velocity is low AND air rate is high, THEN decrease frothdepth; ELSE IF froth velocity is low AND air rate is low/medium,THEN increase air rate; ELSE do nothing.

One area inwhich AI has been used extensively is the identificationand categorisation of froth images from machine vision. Aldrich et al.(1997) trialled and compared inductive learning techniques with aback-propagation neural net method in industry; with all methodsbeing found to be equally capable of classification of various frothfeatures. Similarly, Cipriano et al. (1998) used rule-based expertcontrol, combined with machine vision, to control rougher cells. Thesupervisory controller was able to identify froth characteristics andsubsequently suggest actions to be taken to the operators. Morerecently, Supomo et al. (2008) reported on the successful use of thecommercial control software VisioFroth on PT Freeport, in Indonesia.The system combines expert control with machine vision to controlmass pull, and has reportedly resulted in increased recovery.Similarly, PlantVision by KnowledgeScape also makes use of expertcontrol.

In a comparison between expert control and multivariable model-based control methods, Zavala et al. (1995) compared a supervisorysystem of multiple SISO PID regulators, a multivariable model-basedpredictive controller and an expert rule-based controller on asimulated flotation system. The findings included that: the expertsystem readily became saturated, the PID controllers were difficult totune, while the model-based controller required a linear model of thesystem; the suitability of such amodel to handle all disturbance types/magnitudes being unlikely. Tighter control was obtained with themodel-based controllers. Subsequent studies by Pérez-correa et al.(1998) and Osorio et al. (1999) altered themodel-based controllers tobecomemore flexible to varying conditions (although a high degree ofon-line mathematical manipulation was required); and altered theexpert controllers to avoid control saturation while achieving highrecoveries — despite severe simulated input disturbances.

The combination of AI and model-based methods is also possible.Cubillos and Lima (1997, 1998) noted that using ANN to modelflotation systems is problematic (due to the many associated degreesof freedom and the heavy computational requirements). However,such issues are averted, and the full advantages of ANN realised, byimplementing neural network systems into predictive model-basedcontrol systems, such that the AI system is responsible for updating/modifying constants within the model with time; thus allowing thehybrid controller to remain adaptive. Testing of this hybrid controlstrategy offered promising results, with the controller reportedlybeing robust and flexible.

5. Optimising flotation control

The upper-most tier of process control in flotation is OFC, which bydefinition aims to maximise the financial feasibility of the process.This is achieved by determining where on the theoretical grade–recovery curve is most profitable to operate and, subsequently, shiftingthe operating point orthogonally to further maximise profit (see Fig. 5).

Fig. 5. Grade–recovery curve illustrating optimising control objective (Wills andNapier-Munn, 2006).

67B.J. Shean, J.J. Cilliers / International Journal of Mineral Processing 100 (2011) 57–71

This in turn creates recovery and/or concentrate grade setpoints for thelower AFC/base level control structures (Laurila et al., 2002). However,OFC should only affect lower control levels if the process is at steadystate (McKee, 1991).

Grade–recovery curves vary according to feed grade and can bealtered through manipulation of plant operating variables such as airrates. In this way, grade–recovery curves can be optimised (Neethlingand Cilliers, 2008). An example of this is presented by Hadler andCilliers (2009), whereby the grade–recovery curve for a bank of fourrougher cells was optimised by maximising the stability of the frothwithin each cell.

The use of froth stability as a parameter was first presented byMoys (1984), who published a study whereby the horizontal velocityof the froth could be related to the froth stability, α (also defined asthe air recovered into the launder in the form of unbroken bubbles).Using this quantitative measure of froth stability, Woodburn et al.(1994) developed a semi-empirical froth-based flotation model thatcombines a conceptual froth structure with the kinetics of flotation;the latter being based on the flux of bubble surface area overflowingfrom the cell (see Eq. (1)).

ΨB = αQaSb≈ ζvfhwÞSbð ð1Þ

In this model the flux of bubble surface area (ΨB) is calculatedfrom the volumetric air flowrate into the cell (Qa), the specific bubblearea (Sb) and the air recovery, α. This is approximately the same asrelatingΨB to the specific bubble area, froth velocity (vf), froth height(h), weir length (w) and the volume fraction of the froth that is air (ζ),usually taken as unity. This relationship was simplified by Barbianet al. (2003), who suggested the value of α can be calculated byEq. (2).

α≈ζvf hw=Qa ð2Þ

Barbian et al. (2003, 2005, 2006) measured froth stability atlaboratory and industrial scales using two different methods; airrecovery and the Froth Stability Column. The air recovery wasdetermined using image analysis to measure the froth velocity, withthe overflowing froth height being measured visually. The FrothStability Column is an alternative measure of froth stability compris-ing a non-overflowing column in which the froth rises unhindered.The rate of froth growth andmaximum froth height achieved are usedto give a quantitative measure of froth stability. A good correlationwas shown between the two froth stability measures, suggesting thepotential for both as future measures of froth stability at an industrialscale. Of greater interest, however, were the results shown by Barbianet al. (2006), where a peak in froth stability was shown as the airflowrate increased. A peak in air recovery (or ‘PAR’) was also shown inthe study by Hadler and Cilliers (2009), in which air flowrate was

varied to four cells in a rougher bank, and the air rate that yielded thePAR identified in each of the cells. Moreover it was shown thatoperating the cells at their PAR air rates resulted in a higher mineralrecovery being obtained.

The link between operating cells at PAR air rates and improvedflotation performance was also shown in studies presented by Smithet al. (2010) and Hadler et al. (2010), where it was shown thatoptimising air recovery in a bank of cells resulted in either a higherconcentrate grade, a higher mineral recovery, or in many cases both.This has important implications for control as air recovery is a singlequantitative variable that can be measured and maximised; and theprocess optimised in terms of grade and recovery.

Other methods of OFC exist; some using modelling and othersresorting to expert control methods.

5.1. Modelling-based methods in optimising flotation control

Many model-based optimising flotation controllers are algorithmsthat locate the optimal operating point on the grade–recovery curve;and then present recovery and grade setpoints to lower controlsystems and/or plant operators/management. Flintoff (1992) presentsthe principal of iso-economic contours; which are presented asnegatively sloped straight lines on a grade–recovery curve that arecalculated according to the price of final product, smelting costs,transportation costs, etc. The optimal operating position is then foundby locating the point at which the calculated iso-economic contour istangent to the grade–recovery curve (generated from plant data).

Other modelling-based optimising controllers are more complex,and also consider operating parameter limits and technical details (i.e.not solely economical factors). Muñoz and Cipriano (1999) present amodel-based control strategy that aims to both regulate and optimisea combined primary grinding and flotation circuit. The optimisingbranch of the controller aims to maximise financial profit using non-linear dynamic modelling; encompassing both technical (e.g. millpower limits, sump level limits, etc.) and economic criteria (e.g. metalprices, grinding costs, etc.). More recently, Maldonado et al. (2007),proposed a method “considering phenomenological models for eachflotation bank of the circuit, validated using process data obtained fromseveral sampling surveys.... The control objective is the minimisation ofthe Cu tailing grade in each bank given a final Cu concentrate grade.” Thisis achieved through dynamic programming methods, such that non-linear behaviour can be accounted for; with promising simulationresults being obtained.

5.2. Expert methods in optimising flotation control

Laurila et al. (2002) suggest “new expert systems are concentratingto solve the issue of feed type classification, which is a challenging andimportant task.” It has already been mentioned that the grade–recovery curve is a function of the ore type, and hence it is logical thatoperating parameters and dosing rates should be altered accordinglyif possible. Laine (1995) adds that the performance of such a controlstrategy is decided by the ability of the system to effectively classifythe feed ore, which in turn is dependent on the information receivedby the classification algorithm. The on-line measurement techniquemust extract adequate useful information; whilst the algorithm mustbe able to effectively (and readily) classify ore into one of the definedore type classes. This would allow for a feedforward type controller toalter upstream operating parameters so as to optimally process eachdefined ore type. An example of such an expert system is presented byJämsä-Jounela et al. (2000); where Kohonen self organising maps(a type of ANN) are used for classification of the ore feed informationgathered from instrumentation. Once the ore is classified, the expertcontrol system alters the setpoints of lower control systems and sendsinformation to operators; with the performance being indicated by an“economical success index display”.

Table 3Summary of literature review focusing on various types of control.

Branch Type References

Modelling based Feedback predictive MIMO Hodouin et al. (1993)Feedback and feedforward predictive MIMO Del Villar et al.(1999); Desbiens et al. (1998b); Ding and Gustafsson

(1999); Hodouin et al. (2000); Hulbert (1995); Zavala et al. (1995)Non-linear mathematical modelling Benaskeur and Desbiens (1999); Delport (2005); Desbiens et al. (1998a);

Maldonado et al. (2007)Adaptive control Desbiens et al. (1994); Jämsä-Jounela (1992); Sbarbaro (1999); Thornton

(1991)Modelling/AI based hybrids Hierarchical combination of expert

and modellingBergh et al. (1995); Cubillos and Lima (1997, 1998); Gaulocher et al.(2008); Núñez et al. (2010)

AI based Supervisory control Benford and Meech (1992); Bergh et al. (1996, 1998, 1999); Bergh andYianatos (1999); Cipriano et al. (1991); McKay and Ynchausti (1996)

Fuzzy logic Carvalho and Durão (1999, 2000, 2002); Cipriano et al. (1998); Hirajimaet al. (1991); Osorio et al. (1999); Suichies et al. (1998, 2000)

Neural networks Aldrich et al. (1997); Cortez and Durão (1995); Durão and Cortez (1995);Moolman et al. (1995d)

Inductive learning Aldrich et al. (1997)Integrated flotation and grinding control Modelling and/or AI based Bascur (1991); Muñoz and Cipriano (1999); Pulkkinen et al. (1993); Sosa-

Blanco et al. (2000)

68 B.J. Shean, J.J. Cilliers / International Journal of Mineral Processing 100 (2011) 57–71

However, despite the various OFC strategies outlined in literature,many mineral processing plants currently rely on operators and plantmanagement to manually select setpoints of lower control systems,based on past experience, in order to optimise the process (Laurilaet al., 2002).

6. Examples of approaches found in advanced/optimisingflotation control

Various methods of achieving AFC and OFC have been discussed. Asummary of examples using some of these different advancedmethods are presented in Table 3 (adapted from (Hodouin et al.,2001)). It should be noted that although the methods are divided intogroups, some overlap between methods is inevitable (e.g. adaptivemodel-based controllers are inevitably also classed as predictivecontrollers).

7. Commercial advanced/optimising flotation control software

Various commercial A/OFC systems are available on the marketand have been trialled and implemented in industry; many of whichhave already been mentioned in this communication.

One such control package is the FloatStar suite; which consists ofFloatStar Level Stabiliser, FloatStar pH controller, FloatStar Flow

Fig. 6. Graphical representationof a fuzzy logic generated reagent addition calculation, basedon concentrate and tailings grade by FloatStar Reagent Optimiser (www.mintek.co.za).

Optimiser (these first three have already been discussed), FloatStarGrade–Recovery Optimiser and FloatStar Reagent Optimiser. FloatStarGrade–Recovery Optimiser, an OFC, uses online grade analysis toensure that recovery is maximised for a specified grade; throughmanipulation of level, air flowrate, re-circulating load and reagentaddition setpoints across the plant. FloatStar Reagent Optimiser uses acombination of control approaches (such as fuzzy logic and non-linearmultivariable predictive control) to automate the manipulation ofreagent addition rates (see Fig. 6; www.mintek.co.za).

Several commercial systems make use of machine vision; theseinclude: VisioFroth by Metso (www.metso.com), FrothMaster byOutotec (www.outotec.com) and PlantVision by KnowledgeScape(www.kscape.com). Measured variables include froth velocity, bubblesize distribution, stability and colour. Expert systems are used tomanipulate variables such as pulp level, air addition rate, reagentaddition and/orwater addition in a bid to increase recovery at a set (orimproved) concentrate grade. Each of these systems has beensuccessfully trialled and incorporated on plants. For example, VisioFrothwas successfully implemented on the PT Freeport plant in Indonesia(Supomo et al., 2008) (Table 4).

8. Conclusions

Despite several advances in base level controls since the 1970s,reports of fully automated advanced and optimising flotation controlsystems operating successfully (and unassisted) for long periodsremain scarce. It is hoped, however, that through continueddevelopment of new, robust technologies (e.g. machine vision andair recovery measurement) and the continued simplification/modifi-cations of plant designs (requiring less-intricate control systems),long term, automated advanced and optimising flotation controlwill be achievable. Such an outcome would indeed be financiallyrewarding.

Acknowledgements

The authors would like to thank Prof. Raymond Shaw, Dr. StephenNeethling and Dr. Kathryn Hadler of Imperial College London for theirvaluable input and advice.

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