M Odel - Based Control of Column Flotation : T Oward Industrial Application and Real - Time Optimization

The metallurgical performance of the column flotation process is determined by the concentrate grade and recovery. Although the former can be continuously measured using an on-stream analyzer, the latter must be estimated by a material balance calculation based on seldom verified steady-state assumptions. Consequently, the automatic control and optimization of flotation columns should be performed using secondary variables having a strong influence on the metallurgical performance, such as froth depth, gas hold-up or bubble surface area flux and bias. The aim of this paper is to present a model-based automatic control strategy using froth depth and bias measurements obtained from virtual sensors developed at Laval University. Preliminary pilot plant results on two-phase (water– air) and three-phase system (water – minerals – air) will be presented and eventual industrial implementation of such a strategy as well as the possibility of achieving effective real-time optimization from bias and froth depth measurements will also be discussed. INTRODUCTION Flotation columns have been widely studied and used in mineral processing for about twenty years. Although a great deal of work has been accomplished to understand the influence of different variables such as froth depth (H), bubble surface area flux (Sb) and bias (Jb) on the metallurgical performance, the measurement of some of these variables is still difficult to be performed – except at the laboratory level – because in most cases, no commercial device yet exists. As a result of this, it is almost impossible to achieve neither efficient control nor optimization of the process at the industrial level, even in open-loop operation. This paper aims at presenting a simple control strategy of froth depth and bias. Description of the sensors used to achieved measurements, preliminary pilot plant results for froth depth control, eventual industrial applications and possibilities for process optimization are also treated. BACKGROUND Froth depth and pulp-froth interface position measurements provide the same information about the flotation column operation. Current measuring methods generally attempt to locate the pulpfroth interface instead of directly measuring the froth depth. The pulp-froth interface position is important from a metallurgical point of view because it determines the relative importance of the cleaning and the collection zones. In this paper, both expressions will be used indiscriminately. Many techniques have been proposed in the past for froth depth measurement. The most common ones are summarized by Finch and Dobby (1990) and some further developments are presented by Bergh and Yianatos (1993) and del Villar et al. (1995a, 1995b, 1999). All these methods use the difference of a physical characteristic, such as specific gravity, temperature or conductivity, between the pulp and the froth to locate the pulp-froth interface position. Even though the principles behind these methods are fairly simple, some of them have encountered important operating problems that limit their accuracy. Nevertheless, methods based on the use of a float or pressure gages (one to three) are commonly used and seem to be precise enough for day-to-day process supervision. New techniques using temperature or conductivity profiles have shown promising characteristics for industrial applications. Besides being very accurate, the gathered information can also be used for inferring the bias as indicated hereafter. Conductivity profile probes have been successfully tested by Gomez et al. (1989), Bergh and Yianatos (1993) and del Villar et al. (1999). Moreover, important improvements has been introduced since their inception, namely in what concerns the conductivity profile scanning time, which has improved from one minute (Gomez et al., 1989) to less than one second (del Villar et al., 1999). The bias is another important variable for the column flotation process optimization since it is highly correlated to the concentrate grade for a given reagent dosage and Sb. Defined by Finch and Dobby (1990) as “the net downward flow of water through the froth” or by its equivalent “the net difference of water flow between the tailings and feed” (mass balance calculation around the collection zone), the bias can be qualitatively interpreted as the fraction of the wash water flow really useful for froth cleaning. The wash water flow rate is more often used since simpler, but it does not correlate well to the concentrate grade and recovery. In fact, it also includes the fraction of wash water flow short-circuited to the concentrate which is not used for froth cleaning. Accurate bias measurement with common devices (flow meter and density meter) is difficult to achieve because a steady-state assumption has to be made to obtain the bias from a mass balance calculation (Finch and Dobby, 1990). Moreover, error propagation resulting from the use of four instruments (two flow meters and two density meters) to infer a rather small value, leads to high relative standard deviations (Finch and Dobby, 1990). These facts justify the interest of developing an alternative method. Another approach validated by Uribe-Salas et al. (1991) consists in using a conductivity balance calculation. Known as the "rule of additivity", its final expression :         − − −         − − = w f w c c w f t f t b K K K K J K K K K J J ' ' ' ' ' ' ' (1) involves the knowledge of the tail (J't) and concentrate (J'c) water flow rates as well as the conductivity of wash water flow (Kw) and those of the feed (K'f), tail (K't), and concentrate (K'c) water flows. This method is rather limited to steady-state laboratory scale tests. Moys and Finch (1988) have reported the existence of a relationship between the bias and the temperature profile along the column. An equivalent relationship between bias and conductivity profile has been introduced by Xu et al. (1989) and later detailed by Uribe-Salas et al. (1991). Pérez and del Villar (1996) have proposed a neural network modeling approach to obtain a mathematical representation of the relationship between the conductivity profile and the bias. The use of this sensor for automatic control have been presented by del Villar et al. (1999) and Milot et al. (2000) for a two-phase laboratory column operation. EXPERIMENTAL APPARATUS The pilot flotation column used in this work is 7 m height (23’) and 5.25 cm (2’’) diameter. The column is instrumented with flow meters for feed, tails, wash water and air, as well as with a conductivity profile sensor (eleven 11-cm spaced stainless rings) and conductivity cells on the feed and wash water flows. Local control loops are implemented to regulate feed, tails, wash water and air flow rates. The tests described in this paper were performed on a two-phase system (water – air, no solids) and a three-phase system (water – minerals – air). The feed pulp was composed of an iron ore containing approximately 10 % silicates and a reverse flotation was targeted (silicate flotation). All experiments were conducted at 20 % to 30 % solids. Feed conductivity was adjusted using NaCl for the two-phase experiments. Froth Depth Measurement The pulp-froth interface position is measured using semi-analytical method based on the conductivity profile along the column developed by Grégoire (1997). As described by Desbiens et al. (1998) and del Villar et al. (1999), this approach replaces the previous search of the inflection point of the conductivity profile using a neural network algorithm, thus eliminating the extensive experimentation required for the training of the neural network. In the current method, the various pairs of electrodes (each pair corresponds to a conductivity cell) are sequentially activated to avoid secondary currents and the corresponding conductivity value is calculated through a very precise electronic circuit. The scanning of the whole set of electrodes takes less than one second. Grégoire’s technique is based on the assumption that the resistance of the cell containing the pulp-froth interface is a weighted average of those immediately above (in the froth zone) and below (in the pulp zone). The measurement is achieved in two steps. First, an algorithm locates the cell containing the interface (highest conductivity change). The actual froth depth is then calculated from the conductivity of this cell and that of the immediately adjacent ones (below and above). Figure 1 compares the sensor measurements to the observed values (in a transparent column).