Flotation Column Automation: State of Art

Copyright @ IFAC Automation in Mining, Mineral and Metal Processing, Tokyo, Japan, 2001

FLOTATION COLUMN AUTOMATION: STATE OF ART

L.G. Bergh and J.B. Yianatos

Chemical Engineering Department, Santa Maria University Valparaiso, Chile, [email protected]

Abstract: A review of the state of art and trends in automation and control of flotation columns is presented. Besides the large number of columns installed as a cleaning stage in concentrators world-wide, there is a number of unsolved problems related to lack of instrumentation, lack of process knowledge, odd operating practices, and in general lack of use of data management and processing. In general, proceess control of local objectives are frequently achieved, however, application of mature and new techniques, are rather slowly included in control and information systems. In a near future it is expected that intelligent techniques will be incorporated to solve a large variety of problems. Copyright 02001 1FAC Keywords: automation, process control, modelling, data processing, flotation columns

I. INTRODUCTION

On the other hand, the column concentrate, at least in copper concentrators, is the fmal product (or determines the fmal 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.

Flotation columns (Finch and Dobby, 1990) are now used world-wide as efficient cleaning stages in a large number of sulphide mineral concentrators. More degrees of freedom in their operating variables have led to large variations in metallurgical perfonnance and therefore to much scope for improving their control (Bergh and Yianatos, 1993). Figure 1 illustrates a simplified flotation circuit with all the · variables involved. Usually the feed to the columns consists of the rougher and scavenger concentrates and therefore its flowrate, pH, grade, solids content, solids characteristics (mineralogic species, particle size distribution, degree of liberation) and reagent concentration (PH regulators, collectors, frothers, depressants) are completely determined in the previous stages of grinding, rougher and scavenger flotation, and the conditioning tank. A complete discussion on mineral processing automation can be found in Hodouin et al., (2000).

2. CONTROL APPROACHES 2.1 Control objectives The primary objectives, as indices of process productivity and product quality, are the column recovery and the concentrate grade. The on-line estimation of these indices usually requires significant amount of work in maintenance and calibration of on-stream analysers, in order to maintain good accuracy and high availability (Bergh et a!., 1996). Therefore, it is a common practice to

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into the process from the column feed, temporal malfunction of water and air distributors, instrumentation problems related to calibration, maintenance and failure, and lack of co-ordination in the use of resources such as froth depth, air flowrate and wash water flowrate (Bergh et al., 1996, 1998a, 1999). On line analysers, tailings and feed flowrates and some other measurements are often incorporated into the system when a supervisory control strategy is implemented on top of a distributed control system. A schematic of a control system is shown in Figure 2.

tall

feed

An intermediate approach is the cascade control of

tall

air holdup and air flowrate, and the cascade control of bias and wash water flowrate.

Rougher

Fig. 1 Simplified flotation circuit.

A complete analysis, based on industrial experience, on how improving controllability on flotation columns, relaxing different kind of constraints, as is shown in Figure 3, is presented in Bergh and Yianatos (1999, 2000).

control secondary objectives, such as, feed pH, froth depth, gas flowrate and wash water flowrate (Bergh and Yianatos, 1993). If the secondary objectives are under control and the primary objectives are not measured, cascade control of gas hold-up (using gas flowrate control) and bias (using washwater flowrate control) became intermediate objectives. Froth characteristics, such as color, form, speed, size can also be considered as intermediate objectives, that depends 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.

3. INFORMATION ACQUISITION 3.1 Instrumentation

Orifice plate and dp/cell, and vortex type devices are commonly used to measure air flowrate . Magnetic flowmeters are almost a standard for feed, water and tailing flowrates measurement. Sonic flowmeter devices can also be adequate to handle pulps, however their incorporation is rather slow. Froth depth is usually inferred from pressure measurements with two main problems: scaling and density variation due to changes in solid and air contents in the pulp and froth zones.

2.2 Control organization

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 flowrates and froth depth are measured on line, and tailings, air and wash water flowrates are manipulated. In some circuits pH control and chemical reagent dossification control also are included.

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This control is known as a stabilising strategy. Lack of accurate measurements, non-linear dynamics (Bergh and Yianatos, 1994, 1995) and high interaction among variables are some of the main problems associated with stabilising control. These characteristics reduced the effectiveness of conventional PID control without a supervisor to coordinate the control loops. The use of basic distributed control has frequently lead 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 come from different sources, among them: disturbances coming

reagents

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air tails

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Fig. 2. Distributed and supervisory control system.

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Constraints: • availability • accuracy • repeatability

were these properties has been related to quality indeces as concentrate grade.

Constraints: • Computer hardware • Programming languages • Lack of process knowledge

Density of pulp or solid weight percentage is usually obtained from nuclear density devices. This measurement is 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.

Constraints: • design • layout • specification • installation • maintenance

Chemical reagents as collectors, depresants, 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.

Fig. 3. Improving controllability on flotation columns 3.2 Data management and communications

Since air holdup, 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 et al., (1991), and Perez et al., (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 desadvantage 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.

Real-time and historical information is useful for global plant optimisation. Smart data management systems are required for efficient communication between the business staff (information on metal inventories, costs, production objective, equipment availability ...), 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 trouble-shooting, performance indicator display, real-time optimisation ... ). The availability of the data management 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 porposes are frequently used in concentrators.

The main objetives evaluation requires the measurement or estimation of metal concentration in the feed, the concentrate and the tailings. Devices based on X-ray-fluorescence analysis has been available for more than thirty years. The evolution of these systems has been considering the problem of adequately sampling a pulp, the transportation system of the sample to the detector, and the calibration and cleaning systems in the cells (Leroux and Franklin, 1994). Most system has evolved from high multiplexion to stand alone probes or reduced multiplexion of samples. Even when this is a mature technique the quality and availability of grade measurements are still strongly dependent on the maintenance of the whole system. These difficulties and the high cost of investment and maintenace of these devices have encourage the approach of analysing properties of the froth, view from the top, as an index of performance. Several studies have been made to extract operating parameters from images of the froth (Bonifazi et al., 1999, 2000; Cipriano et al., 1997; van Deventer et al., 1997; Hyotyniemi and Ylin., 1998). These variables are related to the shape, the bright, the color and the transport speed. However, no reports has been found

4. DATA PROCESSING 4.1 Data reconciliation

Because of the inherent inaccuracies of the measurements made, the raw data delivered by the sensors, such as flowrates and chemical assays, contains errors. Data reconciliation procedures are used to correct measurements 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 flowrate and composition of internal streams of a complex unit. Applications to flotation columns have been

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reported by Bergh et al. (1996), and it is expected to grow rapidly with the consolidation of on-stream analysers.

1999b) discussed the control of bias and level in a laboratory column. Carvalho and Durao (1999a) and Bergh et al., (1998b) discussed the performance of a flotation column under fuzzy control.

4.2 Process modelling 5.2 Industrial applications

Dynamic stochastic models (Box and Jenkins, 1976) were experimentally 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 Perez et al. (1993), Bergh and Leon (1997), Carvalho and Durao (1999b).

Hirajima et al. (1991) discussed the application of fuzzy control at Tayoha Mines. Mckay and Ynchausti (1996) reported the application of expert supervisory control. Bergh et al., (1996) presented the implementation and evaluation of hierarchical supervisory control at El Teniente. Bergh et al. (1998a, 1999) discussed the comissioning and evaluation of supervisory control at Salvador. An example of the improvement achieved at Salvador Concentrator is shown in Figure 4, where the performance of the circuit is clearly dependent on the control system.

4.3 Pattern recognition

Historical or real-time sets of measurements on multi variable processes contain massive amount of information about the behaviour of the operation. However, they are difficult to exploit because of the high number of available variables, their poor reliability and fmally 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. Image analysis of froth has been study by Bonifazi et a!. , (1999, 2000); Cipriano et al., (1997); van Deventer et al., (1997); and Hyotyniemi and Ylin, (1998).

6. CONCLUSIONS Flotation columns have been used as part of cleaning circuit for the last two decades. Most of the advances in control has occurred in the last years in the form of expert supervisory systems. These systems rely on key measurements representing the global and local objectives of the process, therefore fault detection and data validation are important issues. Fuzzy logic and ANN has probed to be powerfull tools to be incorporated in these systems. Image analysis of froth has been very active but no control applications are expected until these characteristics can be connected to grade and recovery and how they are affected by the usual manipulated variables.

4.4 Process supervision, fault detection and isolation

In general, some methods are emerging to detect either sensor biases or model inadequacy using multivariable statistical tests on the residuals of material balance constraints (Berton and Hodouin, 2000; Hodouin and Berton, 2000). ANN are also active methods to detect and diagnose faults. Supervision of the control strategy for processes as flotation columns is used to detect sensor or operating problems using data validation and expert systems (Bergh et al., 1999).

ACKNOWLEDGEMENTS The author would like to thanks Conicyt (Project Fondecyt 1990859) and Santa Maria University (Project 270122) for their fmancial support.

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5. CONTROL APPLICATIONS ~

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Control strategies for flotation columns has been discussed by Finch and Dobby (1990) as basic local control. Bergh and Yianatos (1993) proposed supervisory hierarchical control, and Karr (1996) proposed adaptive control.

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200

300

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5.1 Pilot scale applications

Identification and gain-scheduled control is reported by Desbien et al., (1998). Del Villar et al. (1999a,

Fig 4. Performance at Salvador under supervisory control.

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