Flotation column automation state of the art.pdf

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Control Engineering Practice 11 (2003) 6772

Flotation column automation: state of the art

L.G. Bergh*, J.B. Yianatos

Chemical Engineering Department, Santa Maria University, Valparaiso, Chile

Received 18 February 2002; accepted 16 April 2002

Abstract

A review of the trends and state of art in automation and control of flotation columns is presented. Besides the large number of

columns installed in concentrators world-wide, there are a number of unsolved problems related to lack of instrumentation, lack of

process knowledge, odd operating practices, and in general, lack of management and data processing. Process control of localobjectives is frequently achieved, however, application of mature and new techniques, are rather slowly included in control and

information systems. In the near future, it is expected that intelligent techniques will be incorporated to solve a large variety of

problems. r 2003 Elsevier Science Ltd. All rights reserved.

Keywords: Automation; Process control; Modelling; Data processing; Flotation columns

1. Introduction

In the last two decades the use of pneumatic flotationcolumns became wide-spread throughout the mineral

processing industry of metallic, non-metallic and coal

ores in the world. Columns out perform conventional

mechanical cells in cleaning operations (better product

grade) due to their particular froth operation (Finch &

Dobby, 1990).

Fig. 1 shows the classical flotation column design

consisting of two principal zones: the collection zone

and the froth zone. The pulp feed enters near the top of

the collection zone. Hence, particles are contacted

counter-currently with air bubbles generated near the

bottom of the column. Hydrophobic particles collide

and adhere to the bubbles, and they move upwards to

the pulp/froth interface. The froth zone is a mobile

bubble bed, approximately 1 m in froth depth, which is

contacted counter-currently with wash water (added

near the overflow level). Some of the wash water is

recovered into the concentrate overflow, the remainder

providing a net downward flow rate called a positive

bias. The wash water plays an important role in

eliminating fine particles entrainment from the concen-

trate. However, a significant interaction occurs among

operating variables such as froth depth, air and washwater flow rates. The air flow rate is one of the most

sensitive variables which directly affects the air holdup,

mineral recovery and product grade.

Fig. 2 illustrates a simplified flotation circuit with all

of the variables included. The column feed character-

istics such as flow rate, pH, values grade, solids content,

mineralogy, particle size distribution, liberation and

reagent concentration (collectors, frothers, etc.) are

usually determined from previous grinding operations,

flotation stages (rougher and scavenger) and condition-

ing tanks. A complete discussion on mineral processing

automation can be found in Hodouin, J.ams.a-Jounela,

Carvalho, and Bergh (2000).

More degrees of freedom in operating variables have

led to large variations in metallurgical performance and

have therefore provided much scope for improving their

control (Bergh & Yianatos, 1993).

On the other hand, the column concentrate, at least in

copper concentrators, is the final product (or determines

the final characteristic of the products), and therefore its

commercial value depends on the content of copper and

iron as sulphide components. When more than one

valuable metal is presented in the ore then a more

complex flotation circuit is needed.*Corresponding author. Tel.: +56-32-654-229; fax: +56-32-654-478.

E-mail address: [email protected] (L.G. Bergh).

0967-0661/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved.

PII: S 0 9 6 7 - 0 6 6 1 ( 0 2 ) 0 0 0 9 3 - X


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2. Control approaches

2.1. Control objectives

The primary objectives are column recovery and

concentrate grade, which represent the indices of process

productivity and product quality.

The on-line estimation of these indices usually

requires a significant amount of work in maintenance

and calibration of on-stream analysers, in order to

maintain good accuracy and high availability (Bergh,

Yianatos, & Cartes, 1996). Therefore, a common

practice is to control secondary objectives, such as pH

at the feed, froth depth, air flow rate and wash water

flow rate (Bergh & Yianatos, 1993). These are usually

implemented as local controllers or under DCS.

Ideally, when primary objectives are measured, the

control strategy is to change the set points of the

controllers under DCS, in order to achieve good processperformance. This is usually implemented in the form of

expert systems.

If the secondary objectives are under control and the

primary objectives are not measured, cascade control of

gas hold-up (using gas flow rate control) and bias (using

wash water flow rate control) became intermediate

objectives. Froth characteristics, such as colour, form,

speed and size which can also be considered as

intermediate objectives, depend on the regulation of

the secondary objectives and the characteristics of the

feed. In both cases the problem is how to relate these

intermediate objectives with concentrate grade and

process recovery.

2.2. Control organisation

Stable operation of flotation columns and consequent

consistent metallurgical benefits can only be obtained if

basic distributed control systems are implemented. In

general, at least wash water and air flow rates and froth

depth are measured on line, and tailings, air and wash

water flow rates are manipulated. In some circuits pH

control and chemical reagent dosage control are also

included.This control is known as a stabilising strategy. Lack

of accurate measurements, non-linear dynamics (Bergh

& Yianatos, 1994, 1995) and high interaction among

variables are some of the main problems associated with

stabilising control. These characteristics reduce the

effectiveness of conventional PID control without a

supervisor to co-ordinate the control loops. The use of

basic distributed control has frequently led to a large

variability in the concentrate grade and recovery, as can

be observed in many concentrators world-wide. The

contribution to this variability usually comes from

different sources, among them: disturbances coming

into the process from the column feed, temporal

malfunction of water and air distributors, instrumenta-

tion problems related to calibration, maintenance and

failure, and lack of co-ordination in the use of resources

such as froth depth, air flow rate and wash water flow

rate (Bergh et al., 1996; Bergh, Yianatos, Acu *na, L!opez,

& P!erez 1998a, 1999).

On-line analysers, tailings, feed flow rates and some

other measurements are often incorporated into the

system when a supervisory control strategy is imple-

mented on top of a distributed control system. A

schematic of a control system is shown in Fig. 3.

Fig. 1. Flotation column.

FeedTank

Multipurpose

Regrinding

tail

Scavenger

Rougher

wash water

air

feed

tail

feed

concentratereagents

Fig. 2. Simplified flotation circuit.

L.G. Bergh, J.B. Yianatos / Control Engineering Practice 11 (2003) 677268


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An intermediate approach is the cascade control of air

hold-up and air flow rate, and the cascade control of

bias and wash water flow rate.

A complete analysis, based on industrial experience,

on how to improve controllability on flotation columns,

relaxing different kind of constraints, as shown in Fig. 4,

is presented in Bergh and Yianatos (1999, 2000).

3. Information acquisition

3.1. Instrumentation

Orifice plate and dp/cell, mass flow meters and vortex-

type devices are commonly used to measure air flow

rate. Magnetic flow meters are almost a standard for

feed, water and tailing flow rates measurement. Sonic

flow meter devices can also be adequate to handle pulps,

however, their incorporation is rather slow.

Froth depth is usually inferred from pressure mea-

surements with two main problems: scaling and

apparent density variation due to changes in solid and

air contents in the pulp and froth zones. Since air hold-

up, froth depth and bias cannot be measured directly,these variables have to be inferred from other measured

variables. Studies conducted in pilot scale, using either

electrical conductivity and temperature probes, were

reported early by Moys and Finch (1988), and

complemented by Bergh and Yianatos (1991), Uribe-

Salas, G!omez, and Finch (1991), and P!erez, del Villar,

and Flament (1993). Early developments were oriented

to the use of theoretical and empirical models to infer

the variables. Later, artificial neural networks were used

to obtain a model. The main disadvantage found was

the maintenance and recalibration program necessary to

keep the quality of the estimation over time. Presently

no industrial application has been reported using such

approach.

The main objectives evaluation requires the measure-

ment or estimation of metal concentration in feed,

concentrate and tailings flow rates. Devices based on X-

ray-fluorescence analysis has been available for more

than 30 years. The evolution of these systems has

considered the problem of sampling pulp adequately the

transportation system of the sample to the detector; and

the calibration and cleaning systems in the cells (Leroux

& Franklin, 1994). Most systems evolved from high

multiplexing to stand alone probes or reduced multi-

plexing of samples. Even when this is a maturetechnique the quality and availability of grade measure-

ments are still strongly dependent on the maintenance of

the whole system. These difficulties and the high cost of

investment and maintenance of these devices have

encouraged the approach of analysing properties of

the froth, viewed from the top, as an index of

performance. A device based on a video camera

provides images of the froth, where characteristics such

as shape, bright, colour and transport speed can be

estimated on-line. For example, Cipriano, Guarini,

Soto, Briceno, and Mery (1997), described an industrial

device.

Density of pulp or solid weight percentage is usually

obtained from nuclear density devices. This measure-

ment is also limited by calibration and rarely included in

the whole control strategy.

Particle size distribution or some other physical

property of the solids or the pulp are not usually

available for these streams.

Chemical reagents as collectors, depressants, frothers

are usually added before the cleaning stage. pH

measurement is important and its regulation is usually

made independent of the flotation column operation, in

the previous stages of the process.

C1

FIC

wash waterAI

pHC

FIC

air

LICfeed

concentrate

tails

reagents

AI

AI

FI

Supervisor

Fig. 3. Distributed and supervisory control system.

Supervisory

ControlInstrumentation

Information

Process

Constraints:

availability

accuracy

repeatability

Constraints:

design

layout specification

installation

maintenance

Constraints:

Computer

hardware

Programming

languages

Lack of

process

knowledge

Fig. 4. Improving controllability on flotation columns.

L.G. Bergh, J.B. Yianatos / Control Engineering Practice 11 (2003) 6772 69


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3.2. Data management and communications

Real-time and historical information is useful for

global plant optimisation. Smart data management

systems are required for efficient communication be-

tween the business staff (information on metal inven-

tories, costs, production objective, equipmentavailabilityy), the process engineers (information for

production optimisation and control), the laboratory

(quality control), the environment department, and the

operators of the various units. In addition to the data

exchange facilities, the format of the information must

be easily adapted to the various objectives of data

processing (local control, loop tuning, mass balance

calculation, process modelling, maintenance and trou-

ble-shooting, performance indicator display, real-time

optimisationy). The availability of the data manage-

ment architectures and their benefits is extensively

described by Bascur and Kennedy (1999). Innovative

communication systems between remote locations

are emerging. Distributed control systems and PC

networks for control purposes are frequently used in

concentrators.

4. Data processing

4.1. Data reconciliation

Because of the inherent inaccuracies of the measure-

ments made, the raw data delivered by sensors, such as

flow rates and chemical assays, contains errors. Data

reconciliation procedures are used to correct measure-

ments and make it coherent with prior knowledge about

the process. Frequently, mass conservation equations

are used as a basic model to reconcile redundant data

with prior knowledge constraints (Crowe, 1996). At

the same time, data reconciliation techniques may be

used to infer unmeasured process variables such as

flow rate and composition of internal streams of a

complex unit. Applications to flotation columns have

been reported by Bergh et al. (1996), and it is expected

to grow rapidly with the consolidation of on-stream

analysers.

4.2. Process dynamic modelling

Simulation studies based on column flotation

models including process dynamics have been developed

(Sastry & Lofftus, 1988; Pate & Herbst, 1989).

Experimental dynamic stochastic models (Box &

Jenkins, 1976) were obtained for pilot scale flotation

columns by Bergh and Yianatos (1994, 1995) and by

Carvalho (1998). Empirical models to estimate process

variables using artificial neural networks have been

reported by P!erez, del Villar, and Flament (1993), Bergh

and Le!on (1997), Carvalho and Dur*ao (1999b).

4.3. Pattern recognition

Historical or real-time sets of measurements on

multivariable processes contain massive amount ofinformation about the behaviour of the operation.

However, they are difficult to exploit because of the

high number of available variables, their poor reliability

and finally the lack of measurements for the most

important properties as mentioned above. Statistical or

AI techniques are, in general, active or emerging to

extract from these data sets, pieces of information which

may be useful for monitoring, predictive maintenance,

diagnosis, control and optimisation. Several studies

have been made to extract operating parameters from

froth images (Bonifazi, Serranti, Volpe, & Zuco, 1999;

Bonifazi, Serranti, & Volpe, 2000; Cipriano et al., 1997;

Van Deventer, Bezuidenhout, & Moolman, 1997;

Hyotyniemi & Ylinen, 1998; Sadr-Kazemi & Cilliers,

1997, 2000). These variables are related to the shape, the

brightness, the colour and the transport speed. How-

ever, no reports have been found where the absolute

values of image features have been successfully related

to quality indices such as concentrate grade.

4.4. Process supervision, fault detection and isolation

In general, some methods are emerging to detect

either sensor biases or model inadequacy using multi-

variable statistical tests on the residuals of materialbalance constraints (Berton & Hodouin, 2000; Hodouin

& Berton, 2000). ANN are also active methods for fault

diagnosis and detection. Supervision of the control

strategy for processes as flotation columns is used to

detect sensor or operating problems using data valida-

tion and expert systems (Bergh, Yianatos, Acu *na, P!erez,

& L !opez, 1999).

5. Control applications

Control strategies for flotation columns has been

discussed by Finch and Dobby (1990) as basic local

control. Bergh and Yianatos (1993) and Bergh,

Yianatos, and Acu *na (1995) proposed supervisory

hierarchical control, and Karr (1996) proposed adaptive

control.

5.1. Pilot scale applications

Identification and gain-scheduled control is reported

by Desbiens, del Villar, and Milot (1998). del Villar,

Gr!egoire, and Pomerleau (1999a, b) discussed the

control of bias and level in a laboratory column.



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Carvalho and Dur*ao (1999a) and Bergh, Yianatos, and

Leiva (1998b) discussed the performance of a flotation

column under fuzzy control.

5.2. Industrial applications

Hirajima et al. (1991) discussed the application offuzzy control at Tayoha Mines. Mckay and Ynchausti

(1996) reported the application of expert supervisory

control. Bergh et al. (1996) presented the implementa-

tion and evaluation of hierarchical supervisory control

at El Teniente. Bergh, Yianatos, Acu*na, P!erez, and

L !opez (1998a, 1999) discussed the commissioning and

evaluation of supervisory control at Salvador. The

supervisory control was based on an expert system

where the main objectives were to keep the concentrate

grade in a band and the cleaner circuit recovery over a

limited value. To achieve that, concentrate grade was

measured every 10 min on-line, and procedures to

hierarchically change the set points of the depth froth,

air flow rate and wash water flow rate controllers were

implemented. The control strategy considered the

temporal failure of key measurements. Before the

control strategy was implemented, a complete previous

diagnosis of the whole instrumentation and auxiliary

equipments was performed. Sensor calibration and

maintenance problems were solved, operating proce-

dures were uniform and operating personnel in the plant

and control room were trained. This methodology has

been shown to be very successful in maintaining

supervisory control over long periods of time. An

example of the improvement achieved at SalvadorConcentrator is shown in Fig. 5, where the concentrate

grade is kept in a narrow band in spite of changes in feed

grade for more than a year of operation.

This diagnosis methodology, followed by remediation

and relaxation of instrumentation and process con-

straints, previous to the development and implementa-

tion of the supervisory control strategy, can also be

applied to other complex processes.

6. Conclusions

Flotation columns have been used as part of cleaning

circuits for the last two decades. The original control

strategies based on indirect targets such as water bias or

gas holdup have been replaced by direct measurement of

concentrate grades. Most of the advances in controlhave occurred in the last few years in the form of expert

supervisory systems. These systems rely on key measure-

ments representing the global and local objectives of the

process, therefore fault detection and data validation are

important issues. Fuzzy logic and ANN have proved to

be powerful tools to be incorporated into these systems.

Image analysis of froths has been very active but no

automatic control applications are expected until the

derived parameters can be correlated with concentrate

grade and recovery, and the usual manipulated

variables.

Acknowledgements

The authors would like to thank Conicyt (Project

Fondecyt 1020215) and Santa Maria University (Project

270122) for their financial support.

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Flotation column automation state of the art.pdf