Contemporary advanced control techniques for flotation plants with mechanical flotation cells – A review

Minerals Engineering 70 (2015) 228–249

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Minerals Engineering journal homepage: www.elsevier.com/locate/mineng

Contemporary advanced control techniques for flotation plants with mechanical flotation cells – A review Ivana Jovanovic´ a,1, Igor Miljanovic´ b,⇑ a b

Mining and Metallurgy Institute, Bor Mineral Processing Department, Zeleni Bulevar 35, Bor, Serbia University of Belgrade, Faculty of Mining and Geology, Department of Applied Computing and System Engineering, Djusina 7, Belgrade, Serbia

a r t i c l e

i n f o

Article history: Received 20 February 2014 Accepted 30 September 2014

Keywords: Flotation Model predictive control Intelligent control

a b s t r a c t Successful control of flotation plants in modern conditions represents a challenging and complex task that has yet to be accomplished. There have been multiple attempts, however, to find an appropriate control technique that would completely cover the dynamic, complex and poorly-defined flotation system. This paper presents a literature review of current theoretical and applied researches in the field of control of flotation plants with mechanical flotation cells. Significant aspects of the stratification of control levels are described in the paper, with emphasis on advanced techniques that include predictive and intelligent control methods. Traditional PID controllers are found not suitable for the comprehensive control of dynamic flotation systems, except, in part, for the lower hierarchy levels. In the area of advanced control, model predictive methods can improve flotation process performances, but as a rule, in a short period of time. Intelligent methods are playing a significant role in flotation process control, increasing its flexibility, although none of the available variations completely satisfy all the process control aspects. Bearing in mind the results achieved so far, further improvements are expected in the areas of overall control strategy, individual control components and repositioning of advanced control methods. Ó 2014 Elsevier Ltd. All rights reserved.

Contents 1.

2.

3. 4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1. Basic concept of flotation process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2. Flotation variables in terms of process control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stratification of levels and control objectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Types of flotation models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Hierarchy of control levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1. Two-level stratification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2. Three-level stratification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3. Four-level stratification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4. Five-level stratification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Selection of control strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A brief review of conventional control methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Predictive control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Concept, structure and algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Model predictive control in flotation system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1. Some of the proposed MPC strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2. MPC as a tool for optimization and profit maximization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3. A somewhat different approach to MPC of flotation system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

⇑ Corresponding author. Tel.: +381 11 3238 564, +381 63 7 626635; fax: +381 11 3347 934. E-mail addresses: [email protected] (I. Jovanovic´), [email protected] (I. Miljanovic´). 1 Tel.: +381 60 7181 008. http://dx.doi.org/10.1016/j.mineng.2014.09.022 0892-6875/Ó 2014 Elsevier Ltd. All rights reserved.

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5.

6. 7.

4.2.4. Commercial application of MPC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Intelligent control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. A need for more sophisticated control methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1. Expert systems within the intelligent control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Machine vision systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1. Briefly about the froth features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2. Some of the proposed Machine Vision strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3. Commercial application of Machine Vision. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3. Control methods beyond rules of classical logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary discussion about the control methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction

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236 237 237 237 238 239 239 240 242 246 247 247 247

process (Shean and Cilliers, 2011; Laurila et al., 2002). According to Wright (1999) they can be grouped into three categories:

1.1. Basic concept of flotation process Flotation process, where the separation of minerals from complex ores is based on the difference in their hydrophobic features, is performed at the contact of three phases: solid–liquid–gaseous. In this three-phase system (usually, this system consists of water, a solid phase (mineral particles), and the air) hydrophobic particles adhere to gas bubbles that form a particle-air aggregate. The aggregates are lighter than water, and travel upwards to the pulp surface, creating the flotation froth. For metallic mineral raw materials, the concentrate (in some cases the middling) is commonly extracted through the flotation froth, and the rest of the material presents a waste product (i.e. flotation tailings). Although this process appears to be relatively simple, there are also other sub-processes occurring simultaneously in this three-phase system. Examples include: entrainment of gangue minerals into the froth phase, coalescence of air bubbles, and detachment of valuable particles from the bubbles as they impact the froth phase, etc. (Shean and Cilliers, 2011; C´alic´, 1990; Drzymala, 2007). The composition of feed ore (i.e. the type and amount of the useful and gangue minerals) as well as particle size distribution after grinding, can significantly affect the flotation process (Miloševic´, 1994). According to literature data, it is estimated that there are approximately 100 variables that affect (to varying degrees) the flotation

 Feed characteristics (mass flowrate, mineral composition, liberation size, particle size distribution, specific gravity, etc.)  Physicochemical factors (water quality, temperature, reagent types and concentrations, interactions between reagents and particles, etc.)  Hydrodynamics (flotation circuit design, cell type, aeration rate, spatial distribution of bubbles and particles, etc.) Taking into account the number of process variables as well as the diversity of their nature, the real-time flotation process is considered quite complex. Furthermore, the mutual interaction between these variables not only affects the possibility of process control, but makes it more difficult to achieve the desired outcomes. For example, an increase in air flow rate can result in larger bubble sizes, which subsequently affect the bubble rise velocity, rate of attachment, froth depth, etc. (Shean and Cilliers, 2011; C´alic´, 1990). 1.2. Flotation variables in terms of process control There are two dominant parameters to consider, from the production point of view, when evaluating flotation concentration performance: (1) the quality of the final product (i.e. concentrate

DISTURBANCE VARIABLES • Particle size distribution • Surface properties of mineral particles (oxidation degree, useful minerals distribution, etc.) • The percentage of solid in the feed stream • Feedrate

MANIPULATED VARIABLES • • • •

Reagent additions Pulp level in flotation cells Air addition Wash water (eventually)

FINAL CONTROLLED VARIABLES

Flotation concentration

• Concentrate grade and flowrate • Tail grade and flowrate • Circuit recovery

INTERNAL STATE VARIABLES • • • •

Quantity and quality of intermediary products The percentage of solid in the intermediary streams Hydrophobicity of mineral particles Transfer of mineral particles from the pulp to froth and vice versa • Froth properties (mineralization, depth, stability) Fig. 1. Process variables in flotation plant [adapted from Hodouin (2011), Miljanovic´ (2008b)].

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Metallurgical or economic objectives Optimization model Steady-state Optimizer

Control model

d

u

Set-points ySP

Controller

y Process

Measurement block

230

Fault detection and Measurements isolation Raw

x

Observers

Estimated time averaged variables for optimization

Estimated dynamic variables for control

- Signal filtering - Data reconsiliation - Soft sensors - Image processors Observation model

Fig. 2. Details of the generalized control cycle data processing block (u – manipulated variables; d – disturbance variables; x – variables of the internal condition of the process; y – controlled variables) (Hodouin, 2011).

grade) and (2) the recovery of useful component(s) in concentrate. Van Schaikwyk (2002) states that the concentrate grade and process recovery are the two degrees of freedom that describe flotation circuit targets. Sometimes, in the traditional sense, the evaluation of flotation process performances is based on the recovery of useful components in the rougher concentrate that can be achieved during a certain time interval. Given that the recovery is in proportion to the time of flotation, some authors describe the flotation process only as the time function of recovery (Uçurum and Bayat, 2007). Beside the basic output variables that define performance of the flotation process, there are numerous input variables that can be, according to Hodouin (2011), classified as: (1) manipulated and (2) disturbance variables. The same author also classifies all of output variables as (1) controlled and (2) internal state variables. In compliance with the aforementioned, an adapted schematic representation from Hodouin (2011), and Miljanovic´ (2008b) is given in Fig. 1. The influence of flotation variables (Fig. 1) in varying degrees, and their interactions – on the one side, and distinct tendency of plant operators for achieving the process stability and products quality – on the other side, are conflicted in the domain of process control. Therefore, the control of such a process is highly specific and very complex in real industrial conditions. Some possibilities to overcome this problem lie, perhaps, in the following. According to modern trends in the field of flotation process, it should be acknowledged that the flotation is entering a new era in terms of automation and process control, primarily because of three paradigms:  As flotation schemes become less complex, process safety and stability is decreased, while at the same time, process becomes more ready for regulation and optimization.  The trend of increasing the volume of flotation cells is resulting in a reduced number of necessary measuring points and instruments, and consequently, project simplification. At the same time, the requirement for reliability and precision of instruments is on the increase.  Technology is developing toward the utilization of ‘‘smart’’ measuring and regulation equipment (primarily, the visual process analysis, digital transmission and data processing), thereby providing higher quality information and bigger throughput with the possibility of self-diagnosis (Laurila et al., 2002).

2. Stratification of levels and control objectives The first step in industrial processes control is to carefully define production objectives and constraints. For example, the goal can be merely to maintain process stability at certain fixed values or values that scarcely change. For flotation circuits, the objective is commonly defined through the maintenance of a certain value within the boundaries of the plane defined by curves of concentrate quality and recovery (Hodouin, 2011). After defining control objectives, it is necessary to select and develop an appropriate and reliable model of the process. 2.1. Types of flotation models Tools applied in any of the control circuit segment are based on the process models. Models with specific aims such as data reconciliation, state observation, control, optimization or fault detection (Fig. 2) are defined in various segments of the flotation circuit. Models applied under these circumstances can be classified through different approaches such as (Hodouin, 2011): 1. Empirical (multivariate regression, neural networks, principal component analysis method) or phenomenological (taking into account reaction and transformation mechanisms). 2. Steady-state or dynamic. 3. Deterministic or stochastic. 4. Causal (input–output model) or non-causal (a set of relationships linking process variables, such as mass conservation constraints). 5. Linear or non-linear. 6. Based on mathematical equations or fuzzy rules. Rojas and Cipriano (2011) state that the flotation models are generally grouped into the micro-scale and macro-scale models. Micro-scale models identify all the sub-processes using chemical and physical relationships, all of which are highly complex. Macro-scale models, depending on the approach, are suitable for:  Describing particle-bubble behavior (kinetic or probabilistic models).  Characterization of process parameters using the existing process data (empirical models).

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 Designing plant layouts (static models).  Development of control strategies (dynamic models) (Rojas and Cipriano, 2011). The presented classifications of flotation models are important for the hierarchical concept of the flotation process control, as seen by the authors of this paper. Flotation models are subject of research for many scientists, resulting in a number of various models, however, the emphasis in this paper is not on flotation models, since it opens a different set of questions and problems to deal with. 2.2. Hierarchy of control levels The control of the flotation process means: regulating properties of the feed material and maintaining process parameters according to limitations given. The achievement of this objective is made possible by implementing the control through appropriate structures, i.e. hierarchical levels. Number and interconnection of these structures depend on the author’s point of view. Most authors describe the flotation process control as hierarchical through 3 to 4 levels (Laurila et al., 2002; Gupta and Yan, 2006; Liu and MacGregor, 2008). 2.2.1. Two-level stratification According to Sbárbaro and Villar (2010), the main objective of control systems in every mineral processing plant is to ensure: (a) that information is delivered in a timely manner and (b) that urgent measures are taken in order to maintain stability in the plant. This objective is accomplished through a line of operations, functionally classified as (1) basic (human–machine interface, data collection and processing, communication and regulation) and (2) advanced (process analysis, optimization and fault detection).

Real-time optimization

Set-points: Regulatory variables

Economic factors

Set-points: Secondary variables

2.2.2. Three-level stratification Within the general control systems, Jakhu (1998) discusses regulatory, supervisory and optimization control. The same author state that these three control levels may be linked in such a manner that the lower levels may still operate even if the higher levels do not. The hierarchical classification, functionally depending on the flotation control objectives, is suggested by Liu and MacGregor (2008) as follows: (1) process stabilization by minimizing the frequency of variations and the intensity of disturbances, (2) achieving nominal values of the concentrate grade and recovery and (3) maximization of the business process performances.

Process Controller

Filtered measurements : Primary and secondary variables

Regulatory Controller #1 Regulatory Controller #n

Filtered measurements : Secondary variables

231

2.2.3. Four-level stratification Laurila et al. (2002) have presented flotation process control stratification. The basic level is instrumentation, consisting of sensors, communication pathways and analyzers. The next level is a base control level, consisting of process controllers (pulp level, air flow rate and reagent addition dynamics). Advanced process control takes place on the third level by maintaining the concentrate quality and the recovery within desired limitations, while the whole production-business process is optimized at the fourth level by maximizing the profits. Villar et al. (2010) describe in a similar fashion the stratification of the control layers in a column flotation plant (that can be also applied to flotation plants with mechanical flotation cells). The authors have recognized the following layers or levels: instrumentation and regulatory control, process observation, process control, and real-time optimization (Fig. 3). Gupta and Yan (2006) have presented a rather different classification largely based on the operability of the whole plant: the first level consists of all instrumentation and regulation structures. Stabilization of the process and its optimization by input variables manipulation is accomplished at the second level. The third level consists of maximizing the capacity and limiting the quantity of all middlings, while the fourth level takes up the supervising role for the whole plant. 2.2.4. Five-level stratification By integrating the process and production functions into the unique business environment Flintoff (2002) is then able to identify five separate levels of process control (Fig. 4). The elements – flotation control entrants – are separated in a way that is presented in Fig. 4. The focus of the research lies on the instrumentation technique (sensors, controllers), hardware (PLC, peripherals) and control strategies (algorithms, diagnostics), which is often called the control triad (Flintoff, 2002). It can be concluded that process stratification is an important topic in all discussions concerning process control strategies for a flotation plant. Based on the examples given, there are three classes of stratifications: (1) positional, where the process control is organized through levels connected with the position in technological process, (2) stratification, based on the realization of process objectives, and (3) integrated production-business stratifications. Regardless of stratification type or the control strategy direction, the development of control strategies has already begun to point in a new direction since the end of XX century. The model-based control strategies, expert systems, etc., are thus, being increasingly utilized.

Actuators (Instrumentation )

Process

Process observation

Fig. 3. Mineral processing control layers (Villar et al., 2010).

Sensors (Instrumentation )

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Fig. 4. The integration of process and business functions and a place of the model in the process control domain [adapted from Flintoff (2002)].

2.3. Selection of control strategy Choosing an appropriate control strategy is crucial to process control of a specified flotation plant. Therefore, it is important to identify the advantages and disadvantages of a selected control strategy and to be sure that the strategy is the best solution for control of a given plant. Bergh and Yianatos (2011) have identified critical aspects of control strategies: measurement instrumentation, data reconciliation, pattern recognition, fault detection and diagnosis, soft sensors, process and controller performance monitoring. The successful utilization of knowledge-based control strategies related to grinding and flotation is mostly dependent on the quality of information and process knowledge. For example, Remes et al. (2007) argue that the frequency and accuracy of the plant on-line analyzer data bear high significance for the flotation circuit control. Hodouin et al. (2001) argue that the basic quality of the control and optimization strategies, is a reliable mathematical model describing static and dynamic process characteristics in the whole operational range. There are two reasons why mathematical models are often incompetent: (1) the ore is an extremely complex system, essentially characterized by uncertainties, and (2) the science underlying the sub-processes has been insufficiently studied. Overall, when considering improvements in plant operation and its profitability, advanced control techniques play an increasing role. In a paper published by Jonas and Craw (2012), questions, concerning suitability and sustainability of such techniques in the flotation concentration plants, were presented:  The selected technique is appropriate for the process at the plant. – Is the technique efficiently solving problems in variable process conditions? – Is the selected technique functional with existing instruments and controllers? – Are appropriate effects achievable when compared to other techniques?  Implementation and advanced technique support for modern plants. – Is implementation of expert knowledge assumed? – Can it be implemented rapidly?

– Can advanced control be implemented by utilizing local resources?  Sustainability of the advanced control techniques along with the remote location of the plants. – Is technical support often needed? – Is it possible to provide support in remote locations, developing countries or in understaffed places? The following demands are set before advanced control techniques:  To have stabilizing action (more rapid and better reaction to disturbances).  To participate in process optimization in real time (max/min, achieving the desired objective, calculating the optimal objectives based on expenses or values).  To demand minimal investments for implementation and support (a commercial product with minimal adjustment, easy implementation and effortless support from remote locations). According to these demands, there are also several strategies for solving the problem of flotation plant control:  Classic control techniques (not appropriate for dynamic systems or multiple interactions of plant objects).  Multivariable predictive control (predominantly used in systems with complex dynamics and multiple process interactions).  Intelligent control techniques – expert systems, fuzzy logic (the implementation of predictive control and consideration of complex interactions is particularly complicated with these techniques) (Jonas and Craw, 2012). Based on this classification and other facts listed, it can thus be concluded that clear research directions – regarding process control, models and optimization in flotation plants with mechanical flotation cells – have been identified and established over the last two decades. A review of the research papers available to the authors, with particular emphasis on the advanced control techniques, will be presented in the following sections.

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3. A brief review of conventional control methods Conventional control of the flotation process is performed using traditional proportional-integral-derivative (PID) controllers based on feedback and feed-forward control loops. It is common that PID controllers are implemented by DCS (distributed control systems) operating software. This means that the control system is not centralized but rather composed of interconnected multiple units (Gupta and Yan, 2006, Bergh and Yianatos, 2013). Although Chai et al. (2009) point out that the PID controllers are practical, easy for operation and highly reliable, many authors assert that the PID controllers are not suitable for advanced control of flotation circuits (Pérez-Correa et al., 1998; Shean and Cilliers, 2011; Suichies et al., 2000). According to Desbiens et al. (1994) ‘‘a major problem with controlling flotation circuits by conventional SISO PID loops, arises from the large number of manipulated process variables which are available, while the targets are relatively few (mainly grade and recovery).’’ Some authors consider MIMO controllers more suitable for control of certain segments of flotation process. For instance, Kämpjärvi and Jämsä-Jounela (2003) compared a SISO and MIMO control strategies for a pulp level regulation in flotation bank consisting of six cells. It was thus shown that control performances of the MIMO controllers were significantly better than that of the classical SISO controller. Further, in order to have an insight into performances of PID controllers, different monitoring systems are introduced. JämsäJounela et al. (2003) assert that such systems can improve flotation process control and product quality. Notwithstanding, it seems that PID controllers remain suitable tool only for lower levels of flotation process control. Bergh et al. (1999) state that distributed control systems are not sufficient to accomplish appropriate criteria for concentrate grade, therefore supervisory control systems with different attributes are needed. These systems ‘‘should be adaptable to different computation platforms and should at least consider modules for: validation and reconciliation of process data, detection of operation and instrumentation problems and co-ordination of local control loops under an overall strategy.’’ According to Suichies et al. (2000), the multivariable and highly nonlinear nature of the flotation process, as well as complex dynamics with long time constants and significant delays, render the PID algorithm inappropriate for flotation grade control. Consequently, these authors propose the application of more advanced control strategies. In addition, Osorio et al. (1999) state that ‘‘imperfect knowledge of the phenomenology of flotation and the lack of appropriate and precise instrumentation make supervision and control even more difficult. In these plants, conventional control techniques (PID) have evinced poor performance, a lack of robustness and validity over narrow operating ranges.’’ Other researchers also highlight that the advanced techniques (such as expert and model predictive methods) provide far better results for the control of flotation process (PérezCorrea et al., 1998; Rojas and Cipriano, 2011), while Thwaites (2007) considers that the choice between the PID and model-predictive control depends on one’s understanding of the process and all its interactions. 4. Predictive control

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At each control interval an MPC algorithm attempts to optimize future plant behavior by computing a sequence of future manipulated variable adjustments. The first input in the optimal sequence is then sent into the plant, and the entire calculation is repeated at subsequent control intervals (Qin and Badgwell, 2003). In other words, several ideas behind the concept of Model Predictive Control exist:  Explicit use of the model to predict the process output at future time instants (horizon).  Calculation of a control sequence minimizing an objective function.  Application of suitable strategies for permanent re-evaluation of achieving the desired result with the proper alignment (Camacho and Bordons, 1999). The basic structure of Model Predictive Control is presented in the Fig. 5. According to certain authors (Camacho and Bordons, 1999; Holkar and Waghmare, 2010), the design of the most significant MPC algorithms includes the following strategies: Dynamic Matrix Control (DMC), Model Predictive Heuristic Control (MPHC), Predictive Functional Control (PFC), Extended Prediction Self-Adaptive Control (EPSAC), Extended Horizon Adaptive Control (EHAC) and Generalized Predictive Control (GPC). In addition, model predictive control can be classified as robust and adaptive control (integration between them is also possible) (DeHaan and Guay, 2010). This classification is based on previous knowledge about uncertainty limits of process parameters, or their time variability in the system. Adaptive approach allows retuning of controller parameters and, consequently, control of the processes with time-varying or initially uncertain variables. Therefore, adaptive model predictive control is especially important within the context of flotation control, which is prone to non-linear, complex behavior. As such, many predictive flotation control systems often (but not always) include adaptive control aspects. (Shean and Cilliers, 2011). Table 1 shows summary of some advantages and disadvantages of MPC. A comprehensive overview of the existing industrial implementation of Model Predictive Control is presented in the paper by Qin and Badgwell (2003). These authors emphasize the technical capabilities of different MPC technologies and indicate their advantages and disadvantages through the historical aspect. The same authors also provide a summary overview of linear and nonlinear MPC applications in various industry branches. 4.2. Model predictive control in flotation system According to Lundh et al. (2009) the concept of Model Predictive Control in flotation system includes three main segments: (1) a Past Inputs and Outputs Model

Reference Trajectory +

Predicted Outputs –

Future Inputs

4.1. Concept, structure and algorithms Optimizer

Model Predictive Control (MPC) originated in the late seventies (Camacho and Bordons, 1999). The term does not designate a specific control strategy but relates to a very ample range of control methods: i.e. a class of computer control algorithms that utilize an explicit process model to predict the future response of a plant.

Cost Function

Future Errors Constraints

Fig. 5. Basic structure of MPC (Camacho and Bordons, 1999).

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Table 1 Advantages and disadvantages of MPC [adapted from www.open.umich.edu]. Advantages

Disadvantages

Can be used to handle multivariable control programs Can consider actuator limitations

Some MPC algorithms are limited to only stable, open-loop processes Often requires a large number of model coefficients to describe a response If models are formulated for output disturbances, they may not handle input disturbances well If the prediction horizon is not formulated correctly, control performance will be poor even if the model is correct For the systems with a wide range of operating conditions that change frequently (such as flotation system), MPC using linear process model will not be able to handle the dynamic behavior of the process. A nonlinear model must be used for better control performance –

Can increase profits by allowing for operation close to the system constraints Can perform online computations quickly

Can be used for non-minimal phase and unstable processes

Easy to tune; able to handle structural changes

dynamical model, (2) the combination of revenue function and constraints, and (3) knowledge of the current dynamical state of the process. The future behavior of the process must be predicted using a reliable dynamical model of the flotation circuit, wherein the modeling process is carried out according to one of two paradigms:  First Principles Modeling: i.e. the description of the relationship between variables is performed using equations based on the knowledge of the system, whereby the selected parameters are adaptively adjusted online.  Grey Box Modeling: i.e. models are generated on the basis of plant data. Variables have to pass through a process of ‘‘excitation’’ in order to form a successful algorithm. Linearity of process model plays an important role during the development of a MPC strategy. Linear empirical models have been used in the majority of MPC applications to date, and the most of the current MPC strategies are based on this model type (Qin and Badgwell, 2003). However, taking into account that flotation system is highly nonlinear, many authors agree that the flotation process is better described by nonlinear models (Desbiens et al., 1998; Delport, 2005; Maldonado et al., 2007; Preez et al., 2013). For example, Casali et al. (2002) have developed a nonlinear dynamic model of rougher flotation circuit of sulfide copper ore through the population balance approach. The authors have emphasized that this model could be applied in a GPC control strategy. Desbiens et al. (1998) constructed the predictive control algorithm on the basis of a nonlinear dynamical model of a rougher flotation circuit. The parameters of the nonlinear model are obtained by interpolating the parameters of three local linear models. The same authors reported that: ‘‘since nonlinear algorithm makes use of more information about the process, it exhibits better performances and robustness than linear control’’. On the other hand, Camacho and Bordons (1999) state that developing adequate nonlinear process model can be very complicated and discuss about difficulties of using these models for MPC strategies. In brief, both types of models have their advantages and disadvantages, and application of model type (linear or nonlinear) in flotation control strategy depends on a researcher’s approach.

4.2.1. Some of the proposed MPC strategies As aforementioned, the model predictive control is used instead of more traditional methods of flotation control. For example, a multivariable predictive controller with fixed parameters is tested for control purposes through a simulated flotation circuit by Hodouin et al. (1993). However, the models are usually very sensitive to operating conditions and ore characteristics, therefore Desbiens et al. (1994) prefer adaptive control strategies to model-based fixed controllers, in order to prevent poor performances. Consequently, they report on the application of the GPC algorithm for the control of a simulated rougher flotation circuit. The complex dynamics of the circuit is described by a linear discrete I/O model, where the control variables are the air feed rate and the collector feed rate to ore-feed rate ratio. This distributed adaptive strategy proved to be a much better and simpler solution than multivariable controllers with fixed parameters for the controlling of flotation systems. Zavala et al. (1995) compared three algorithms in the control of a simulated copper flotation plant: (1) a multivariable DMC algorithm, (2) an expert rule-based controller and (3) supervisory system of multiple SISO PI controllers. The best performances are shown by the DMC algorithm, with the only drawback being that its successful implementation requires a reliable linear model of the process, which is extremely difficult to achieve in industrial conditions. Therefore, the authors propose a combined control strategy based on expert and multivariable predictive control. Similarly, Pérez-Correa et al. (1998) assessed expert and predictive multivariable control algorithms for a copper flotation plant with mechanical flotation cells (rougher and cleaning section), through simulations. Simulations were performed using a nonlinear dynamic model, derived from mass balances and empirical relationships that qualitatively and successfully re-produced the dynamic behavior of a real plant. Multivariable predictive control was accomplished applying DMC and QDMC (Quadratic Dynamic Matrix Control) algorithms. The manipulated variables were: collector doses, frother addition rate, pulp level and the considered disturbances: copper feed grade, feed flow-rate, average feed particle size, iron feed content and the feed-stream pH. Measured outputs were: the copper tailing grade, copper concentrate grade and the recovery, while the unmeasured outputs were: concentrate and tailing flow-rates. Some empirical modifications of the flotation kinetics were introduced, so that the addition rates of collector and frother could be taken into account. With the same tuning parameters and set point values, as well as loose constraints on the concentrate and tailing grade (but strict on the recovery), QDMC presented satisfactory performance in maintaining the value of recovery at the desired level (Fig. 6). According to the author’s conclusion, the DMC algorithm has displayed good performances, thus presenting small output deviations and ‘‘smooth’’ control. The QDMC algorithm achieved even better performances, but demanded a more complex mathematical

Fig. 6. Dynamic response of the plant controlled by predictive algorithms [adapted from Pérez-Correa et al. (1998)].

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apparatus than DMC. An important additional advantage of QDMC over DMC was flexibility. However, as in the previous example (Zavala et al. (1995)), the main drawback of DMC and QDMC algorithms was the requirement of a good and reliable linear process model. As a solution to overcome the need for linear process models Gupta and Yan (2006) have suggested an application of more adaptive control algorithms (e.g. EPSAC or EHAC) to use, due to the flotation system’s rather indicative, nonlinear nature. Following the experiments conducted within the Copper roughing and cleaning circuit of Rio Tinto’s Northparkes operation, Runge et al. (2003) concluded that the quality of the products obtained from the flotation cells directly depends on the interaction of two essentially independent property classes: (1) the cell operating conditions (e.g. air flow-rate, froth depth, impeller speed) and (2) the properties of the cell feed stream (e.g. particle size, mineral composition, etc.). They point out that this interaction is the starting basis for development of a model structure that associates a constant set of flotation parameters to the streams of the circuit, which are then transformed into a flotation response depending on the operating conditions within the flotation cell. Such models usually involve determining the model parameters for each process unit (e.g. rougher flotation, scavenger, cleaning). Accordingly, two different types of models for robust predictive control of industrial copper flotation circuit have been developed. The first type of model takes into account the difference between stream and unit operating variable effects, whereas the second type of model, does not consider these differences. The same authors conclude that the second type of model is not suitable for predicting technological performances of a given flotation process (Runge et al., 2003). Hodouin et al. (2000) reported about a predictive controller which was used to assess the advantage of combining feedforward and feedback actions for the control of a simulated copper flotation process. Authors also reported that the feedforward action reduced the effect of flotation feed grade and flowrate variations, and improved the performance of the traditional feedback loop (as shown in Fig. 7). However, they further emphasized that an inadequately tuned feedback compensation can deteriorate the action of a well-tuned feedforward controller (Hodouin et al., 2000). Muller et al. (2010) described the strategy of PGM (Platinum Group Metal) flotation system control, based on three control levels. Base level of control is accomplished by implementing conventional PID controllers (with the purpose of stabilizing process parameters), while at the supervisory level, control of product mass pull is achieved through the use of a fuzzy logic expert controller. Process optimization is realized through model predictive

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control, by applying the DMC algorithm. The control objective of the MPC strategy is focused on maintaining the concentrate grade (measured online) within a desired range by manipulating the mass pull set-points of flotation units. Personnel employed in the Anglo Platinum’s flotation plant have adopted this control solution to deal with the multivariate nature of the process optimization in real time. Due to the consistent operation of flotation cells at the optimal regime, analysis of production data, before and after the installation of this control strategy, has revealed a significant increase in mass pull stability, as well as an increase of overall recovery by 1.71% (for the data obtained from the annual production). It should be noted, however, that Anglo Platinum has a welldeveloped supervisory control layer, tightly integrated into the basic control schema. An integrated APC suite is centered on a G2 based Expert System – the Anglo Platinum Expert Toolkit (APET). The Anglo Platinum flotation mass pull supervisory control layer consists of a fuzzy logic rules-based expert controller that aims to operate the circuit by a desired formula and stabilize both the individual cell and the overall circuit mass pulls. The ideas and realizations expressed in this paper are particularly important because of the hybrid approach to this complex issue. The fuzzy layer is the middle of the ‘‘control sandwich’’, which only adds to the overall model flexibility and plant performance sustainability. Even without the introduction of fuzzyfication at all levels, the improvements made speak in favor of the adopted approach (Muller et al., 2010). 4.2.2. MPC as a tool for optimization and profit maximization The optimization of the flotation process is a very important task in the mineral processing industry, due to the profit fluctuation as a consequence of changes in mineral recovery. Even the 0.5% increase in recovery could be economically significant within the mineral processing industry. (Ferreira and Loveday, 2000). Maldonado et al. (2007) studied the optimal solution for rougher copper flotation circuit control, based on dynamic programming techniques under different operational conditions. The optimization objective was the minimization of the Cu tailing grade in each flotation bank (5 banks in total), given a desired final Cu concentrate grade. Explicit process model describing functional dependence of Cu concentrate grade on considered process parameters was developed by population-balance approach. At the same time, authors defined functional dependence of the optimal profit on the concentrate and tailing grades (for each of the flotation banks). They point out that a described control system ‘‘can be seen as the highest level of a hierarchical control strategy and could be implemented within any regulatory control strategy in a straightforward manner.’’ On the other hand, they indicate that ‘‘one of the several drawbacks of the proposed optimization strategy, is

Fig. 7. Simulation of control strategies [adapted from Hodouin et al. (2000)].

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the large number of states generated, resulting in a high computational load.’’ Muñoz and Cipriano (1999) developed a two-level control strategy, based on the predictive control principles. This control strategy is applied on a simulated copper mineral grinding-flotation plant. The regulatory control level is based on multivariable predictive controllers, using process models. The linear multivariable dynamic models are developed for each grinding system (3 mills in total), linking mill power consumption, percentage of solids and ore particle size distribution with fresh ore feed and sump water addition. At the optimization level, profit maximization is predicted through the application of nonlinear dynamic process models. In flotation system, tailings and concentrate grades are linked with the total product tonnage and a static non-linear function of the average particle size of product, using non-linear Hammerstein-type models. The objective function (F), representing the profit of the grinding-flotation plant (in US$/hour), is formulated as follows: " # Nf Ns X X F¼ b  /  b gb ðk0 þ k þ jf ÞTðk0 þ kÞ  c d W t ðk0 þ k þ jg Þ  C fix 1

k¼1

2

t

i¼1

where k is the discrete time variable; k0 is current time; jg and jf are mineral processing (grinding and flotation) time delays expressed in terms of the number of samples; Nf is prediction horiW t are predictions of zon; Ns is number of grinding sections; gbt and d the tiling grade and the power demand of each mill; c is parameter depending on the costs of consumed steel and electricity during the grinding; b1 and b2 are parameters depending on the revenues drawn from the copper concentration, price of fine copper, copper recovery in smelting and (only for b1) feed grade; / is parameter depending on the consumption and costs of flotation reagents; T is fresh feed rate; Cfix is the plant’s fixed costs. This optimal control strategy has been tested by two approaches (without and with disturbances in mineral hardness). Initially, the plant is operated using a regulatory control strategy for 190 min, at which point the optimizing control strategy is activated. The results, drawn from simulations, showed that the proposed strategy contributed to significant improvement in economic profits when compared against an exclusively regulatory strategy (Fig. 8) (Muñoz and Cipriano, 1999). Rojas and Cipriano (2011) have compared three different control strategies – from the perspective of their performance and economic benefits – which aim to maintain control in rougher flotation in order to maximize the recovery and keep the concentrate grade over a minimum. The first is a non-reactive strategy with a fixed control that does not react to input disturbances, and the other two are model-predictive control strategies (employing linear process models). The first of MPC strategies considers general tail and concentrate grades and the second MPC strategy considers additional estimation of concentrate grade in intermediate cells. According to simulation results, both of MPC strategies are able to maintain higher performance in the presence of disturbances in the process, getting up to 1.7% higher recovery than a process with fixed control. However, the economic benefits

obtained with the second MPC strategy are greater, which indicates about importance of considering the quality of flotation intermediates, during a control strategy development. (Rojas and Cipriano, 2011). 4.2.3. A somewhat different approach to MPC of flotation system In some cases, achieving satisfactory performances of the flotation process control requires the usage of several different strategies. Suichies et al. (2000) have described implementation of the simple GPC algorithm on an expert system, to control the grade of flotation products in industrial plants, for flotation concentration of lead and zinc sulfide minerals. Lundh et al. (2009) have also described the implementation of the MPC controller on an expert system, for the control of zinc flotation circuit in Boliden Garpenberg concentrator, Sweden. Moreover, explicit process models for a predictive control of flotation circuits can be based on the methods of artificial intelligence (Marais and Aldrich, 2011a; Xiaoping and Aldrich, 2013; Al-Thyabat, 2008). Another one interesting approach is proposed by Bushell (2012). The performance of flotation process (expressed by recovery of PGM components) is predicted by mineral composition of feed ore particles. The author states that this procedure provides a means to monitor and troubleshoot plant performance based on ore mineralogy. 4.2.4. Commercial application of MPC According to the authors’ knowledge, one of the most successful advanced systems for flotation control is FloatStar package developed by Mintek. This package comprises of several control modules for process simulation, monitoring, stabilization and optimization. In terms of advanced process control one should note in particular FloatStar Flow Optimiser, FloatStar Grade–Recovery Optimiser and FloatStar Reagent Optimiser. This modules, inter alia, uses principles of model predictive control. FloatStar Flow Optimizer is a multivariable predictive controller whose manipulated variables are the pulp level and air flow references (Cipriano, 2010). FloatStar Grade–Recovery Optimiser uses online grade analysis to ensure that recovery is maximized for a specified grade through manipulation of level, air flowrate, re-circulating load and reagent addition setpoints across the plant. FloatStar Reagent Optimiser uses a combination of control approaches (such as fuzzy logic and non-linear multivariable predictive control) to automate the manipulation of reagent addition rates (Shean and Cilliers, 2011). Successful application of these modules is documented throughout the literature. For example, Mantsho et al. (2013) described performance of FloatStar Grade-Recovery Optimiser (using new, reliable online analyzers) at the BCL (Bougainville Copper Limited) nickel concentrator plant in Botswana. They reported that Grade-Recovery Optimizer provided a consistent improvement in flotation recoveries of nickel, compared to the period when it was switched off. Smith et al. (2005) also reported about efficient testing of FloatStar Grade-Recovery Optimiser at a copper rough flotation circuit in South America. The grade was controlled well

Fig. 8. Plant profit under regulatory and optimizing control [adapted from Muñoz and Cipriano (1999)].

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around its target and the recovery was well above its maximum, as it is shown in Fig. 9. Iorio et al. (2003) have presented a plot of data obtained from a South American copper flotation plant (Fig. 10). The data compares the grade plotted vs the recovery in the cases of the FloatStar Grade-Recovery Optimiser being on and off. The red points represent two weeks of data with the Optimiser being off, while the green points represent two weeks of data with the Optimiser on. According to authors, plant personnel commented that the Optimiser allowed the circuit to maintain final grade control, while maximizing recovery. Lombardi et al. (2012) reported about benefits of FloatStar Flow Optimiser implementation at Prominent Hill copper–gold concentrator, South Australia. Additional data, about good results in control of concentrate and tailings mass flow (that have been achieved by FloatStar Flow Optimiser), are provided by Singh et al. (2003) (for industrial platinum flotation circuit) and Knights et al. (2012) (for iron ore reverse flotation circuit in Cauê Mine, Brazil). Table 2 shows a summary of reported MPC strategies applied in the plants worldwide. It seems that – according to these findings – the major success in development and implementation of MPC strategies is achieved in copper mineral flotation. However, this could also be the consequence of copper flotation plants being the most frequent among those listed below. 5. Intelligent control 5.1. A need for more sophisticated control methods When discussing process operations from the plant performances point of view, a quick, precise and adaptive reaction of

Fig. 9. FloatStar control of grade and recovery on a copper circuit [adapted from Smith et al. (2005)].

Fig. 10. Grade vs recovery for rougher lines of copper flotation plant [adapted from Iorio et al. (2003)].

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Table 2 Reported MPC strategies according to ore type. Type of ore

Reference

Lead/Zinc Copper/ Copper– gold

Lundh et al. (2009), Suichies et al. (2000), Maldonado et al. (2007), Casali et al. (2002), Zavala et al. (1995), Pérez-Correa et al. (1998), Runge et al. (2003), Hodouin et al. (2000), Muñoz and Cipriano (1999), Rojas and Cipriano (2011), Smith et al. (2005), Iorio et al. (2003), Lombardi et al. (2012) Preez et al. (2013), Muller et al. (2010), Marais and Aldrich (2011a), Bushell (2012), Singh et al. (2003) Mantsho et al. (2013) Knights et al. (2012)

Platinum group Nickel Iron

the system is an essential demand. Furthermore, the need for the development and utilization of sophisticated control methods-as well as production flexibility-is even higher given the lack of information available, an existing non-linear environment, as well as the system’s rather complex nature (Karray and De Silva, 2004). One of the possible solutions of this problem is intelligent control. Intelligent control is a discipline where control methods are developed so that they mimic important characteristics of human intelligence. These characteristics comprehend adaptation and learning, planning under high uncertainty, and computing immense quantities of data (Zhang, 2010). Classic control methods require an understanding of work with a complete set of data, including sensor information and values within all process parameters. Unless all the necessary data is completely known, appropriate estimations should always be taken into account. And if the available information is at all fuzzy, qualitative, incomplete or unclear, the classic regulators and control will not provide satisfactory results. Furthermore, classic control techniques are largely based on the assumption that plant operation is linear and time-invariable, which does not correspond to the majority of real processes. Unlike conventional control, intelligent control techniques possess capabilities that effectively deal with incomplete information concerning the plant and its environment, and any unexpected or unfamiliar conditions (Karray and De Silva, 2004). Therefore, it can be asserted that intelligent techniques contribute to the flotation plants control by realizing a systematic approach. It is a relatively new concept by which the flotation plant is considered to be a dynamic system, and system components-and the rules they are subject to-are no longer considered to be completely theoretically supported or defined. At the same time, the optimal system guidance cannot accomplished after solving a single equation or a whole set of equations, but only by means of a system approach and an appropriate response to the real dynamic conditions of input and output variables. Accordingly, intelligent methods (neural networks, fuzzy logic, etc.) are being increasingly utilized through different approaches: i.e. through real time process control, diagnostics, modeling and process analysis, optimization, etc. Although the applications are diverse, analogies can be noted on several levels: i.e. capability for processing imprecise, uncertain and unclear information, utilization of similar inference mechanisms, etc. (Leiviskä, 2001a,b). 5.1.1. Expert systems within the intelligent control The control techniques area is based on expert systems, presenting one of the variants within the conditions of increased complexity, non-linearity and accelerated dynamics of process systems (which is surely applicable for the flotation process). Control strategies based on the human ‘‘experience’’ and related to the plant operation, heuristics, common sense or expert opinion, can often provide new perspective and solutions for problems of this class. Furthermore, intelligent control systems serve as the computer’s

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support network for the purpose of simulating or imitating an expert’s reasoning capability, particularly when it is necessary to make appropriate decisions and take control actions (i.e. decisionmaking support). When considering expert control techniques in flotation systems, high precision flotation plant performances (regardless of the model applied) must be interpreted by experts before their implementation in the decision-making support system. It is well known from the process control practice that significant improvements in system operation can be achieved when expert engineers are involved in the monitoring and controlling process. Operational improvements are achieved through high level control actions (in particular, the action of process parameters tuning) which are conducted by a highly skilled and experienced operator. When the process variables and control points are established or set, the operator can then implement adjustments and other control actions accordingly-as part of the qualitative linguistic rules-based on common sense, heuristic, knowledge and experience rather than classic algorithms (Karray and De Silva, 2004). However, the application of expert systems in flotation control is subject to certain limitations. For example, Gouws and Aldrich (1996) have questioned the availability and high quality interpretation of all experts in the field, even the most subtle disturbances within the plant. They also state the possibility of cognitive prejudices in the direction of process simplification. According to them, it can take months, if not years, to develop a comprehensive expert system for a certain flotation plant; a system which is not only strategically and conceptually complex, but also financially challenging. Similarly, Ghobadi et al. (2011) assert that every control

Table 3 Advantages and disadvantages of expert systems [adapted from Turban (1992)]. Advantages

Disadvantages

Able to capture the scarce expertise of a uniquely qualified expert Able to solve superior problems in a very short period of time Reliability

Knowledge is not always readily available High development costs

Work with incomplete information Transfer of knowledge to the user

Only work well in narrow domains Can not learn from experience Not suitable for solving all the problems

technique has its limitations. Consequently, comprehensive expert method – that would meet control demands of all types of flotation industrial processes – has yet to be developed. Table 3 shows summary of some advantages and disadvantages of expert systems. Keeping the facts and the available literature in mind, intelligent control techniques-that are presently used in flotation systems (whether it is a theoretical approach or a developed commercial strategy)-are predominantly based on the classic logic rules (less common) or the following methods of artificial intelligence: computer vision, neural networks, fuzzy logic, inductive decision trees, genetic algorithms as well as their combination. According to the findings of the authors, artificial intelligence methods have their widest application in the areas of identification and categorization of flotation froth, which have thus been documented through reference data by various researchers. 5.2. Machine vision systems Although the Machine Vision analysis of flotation froth images recognizes the technique that is commonly associated with the basic levels of flotation system control (Shean and Cilliers, 2011), it also represents a form of artificial intelligence implemented into the higher level control strategies. Supomo et al. (2008) point out that the Machine Vision system serves to replace the human visual analysis and ensure the permanent monitoring and quantitative determination of froth properties. Schematic diagram of a typical machine vision system for froth flotation control given by Forbes (2007) is shown in Fig. 11. Usually, the feed ore content, the final concentrate grade and tailing grade are continuously monitored in the flotation plant, while the grades of other flotation stage products are omitted. Consequently, the system of organizing flotation cells in different sections, poses as an obstacle to process optimization. This is the reason therefore, why the same values of the required pulp level are used for different cell sections in some control strategies. Computer vision techniques offer a solution to this problem, by providing information on froth color, average air bubble size, froth texture, froth stability and mobility, etc. (Moolman et al., 1994, 1995a,b, 1996a). However, according to Herbst and Flintoff (2012), despite of many information that froth vision systems can provide, most of them have not yet been worked into process control strategies. They also assert that in terms of added value (in a good control

Fig. 11. Froth Vision System architecture [adapted from Forbes (2007)].

I. Jovanovic´, I. Miljanovic´ / Minerals Engineering 70 (2015) 228–249 Table 4 Pro et contra froth image analysis [adapted from Van Schaikwyk (2002)]. Advantages

Disadvantages

100% availability with low maintenance Relatively low capital costs Measurement available every 2 s Non-intrusive soft sensor Continuous development possible

Does not directlya measure the metallurgical performance of a flotation cell Some camera output measurements (e.g. color) are influenced by ambient light – – –

a There are recently published findings that the grade of platinum flotation processes can be predicted from textural features extracted from froth images (Marais and Aldrich, 2011b).

strategy) froth vision systems have been shown to generate rougher metal recovery improvements of 1.5% or more, at roughly the same concentrate grade. (Herbst and Flintoff, 2012). Similarly to every control method, application of Machine Vision systems has its benefits and drawbacks. In his MSc thesis on Multivariable Control of a Rougher Flotation Cell, Van Schaikwyk (2002) suggests the following advantages and disadvantages of froth image analysis (Table 4). Regardless of some drawbacks, this technique is widely accepted between researchers. The text to follow thus contains general principles of utilization of this technique in flotation systems. 5.2.1. Briefly about the froth features Aldrich et al. (2010) have presented a detailed review of certain features to identify (i.e. important variables for the flotation process) that can be extracted from the flotation froth images, and specific computer methods to use in order to achieve their identification. According to them, froth features can be divided as: (1) physical (bubble size and shape, froth color); (2) statistical (FFT coefficients, wavelet coefficients, textural variables, co-occurrence matrix variables, fractal descriptors, latent variables); (3) dynamic (froth mobility and stability). The authors have also described the possibility of applying these features (obtained from the images of froth in control systems) through extensive literature review, and then providing a subsequent list of several main commercial control systems based on machine vision (Aldrich et al., 2010). Selection of froth feature that will be observed and corresponding computer recognition method depends on the decision of a researcher. For example, Sadr-Kazemi and Cilliers (1997) point out that accurate and rapid detection of the bubble size and shape distribution on the surface of flotation froth represents important requirement for indication of flotation performance. Gui et al. (2013) and He et al. (2013) assert that the surface texture appearance of the flotation froth provides key information on flotation process performances. They have concluded that classical GLCM methods (Gray Level Coocurrence Matrix) for the extraction of froth image texture features are not completely appropriate, and suggested other, more improved methods for the description of flotation froth image texture. Bonifazi et al. (2001) attempted to employ several techniques for flotation froth images processing in automated determination of the color and bubble structure. The flotation froth images were acquired in the Boliden plant of Garpenberg mine, Sweden. Color analysis was performed with the reference RGB, HSV and HSI systems (the systems for representing the RGB system pixel coordinates: RGB – red, green, blue, HSV – hue, saturation, value, HIS – hue, saturation, intensity), while the morphological measurements were performed upon enhancement and segmentation of

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the images. Strong correlations were determined, particularly between the content of Cu, Zn and MgO in the flotation froth, and furthermore the parameters from the digital images, extracted. 5.2.2. Some of the proposed Machine Vision strategies According to Liu et al. (2005) quality visual analysis of the flotation froth should ‘‘provide a rich description of froth morphology, have the ability to handle correlation in RGB color space, have the capacity for resilience in various lighting conditions, and be computationally inexpensive’’. Consequently, it was shown that multi-resolutional multivariate image analysis (MR-MIA) is a suitable tool for visual analysis of the froth. This technique is applied in a zinc recovery section of Agnico-Eagle’s Laronde plant in Quebec to provide information about health of the froth (i.e. bubble size distribution and presence and amount of clear windows or black holes in the froth). Several years later, Liu and MacGregor (2008) showed that resulted obtained by aforementioned MR-MIA technique can be directly used in froth control as well as in froth modeling. Therefore, they developed a method (based on the causal process model) for predicting the appearance of flotation froth, employing MR-MIA features as process outputs (dependent variables) and the other parameters (such as flow rate of CuSO4 and KAX; content of Fe, Cu, Zn, Pb, Ag and solid in the fresh feed) as independent variables. The authors concluded that this method can be used as part of comprehensive flotation control system. Some of the results are presented in Fig. 12 (Liu and MacGregor, 2008). Núñez and Cipriano (2009) have developed a method for the characterization and recognition of visual information on flotation froth surface, using dynamic texture (i.e. sequences of images of moving scenes that exhibit certain stationary properties in time) techniques. In addition, they have developed a froth speed predictor based on dynamic texture model which has the capability of predict froth speed temporal evolution, giving a virtual measurement for expert control of rougher flotation circuit. Xu et al. (2012) have proposed a method of the fault detection for reagent addition in industrial froth flotation process, based on machine vision techniques. The authors were investigating the distribution of bubble size probability and its inference to identify chemical operational status at the bauxite froth flotation site. They have further explored the relationship between the film size distribution and the bubble size distribution. By using the normal kernel approximation, the fault detection problem is solved through a criterion that determines the threshold of the normal residual signal. Desired fault detection for reagent addition in froth flotation industry is achieved using the proposed method. Wright (1999) consider that the size and shape of bubbles that constitute the flotation froth convey important information on the performance of the flotation process. Therefore, he has developed machine vision system that identifies the bubbles in the froth by segmenting the image using Fast Watershed Transform (see Fig. 13). The author assert that bubble size and shape information extracted from the segmented images and can be correlated with metallurgical and other flotation plant data in order to elucidate relationships between froth appearance and plant performance. The machine vision system developed was tested on a platinum concentrator plant, and is able to identify and characterize variations in flotation froth appearance, which occur in response to changes in process inputs (Wright, 1999). Van Schaikwyk (2002) has researched closed loop control system (based on machine vision) on a rougher flotation cell, with the aim to improve PGM flotation performance. He determined linear relationship between machine vision outputs (bubble velocity and bubble color) and the other process variables (such as air addition, pulp level, initial concentrate grade, etc.) The author has

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Fig. 12. Control simulation results [adapted from Liu and MacGregor (2008)].

showing the direction that froth stability parameters and operating variables are required to change to effect a consistent increase in the solids recovery and concentrate grade within the flotation cell under different solid regimes, across a wide range of operating conditions. According to author’s findings, the relationship between froth velocity and flotation performance (under observed conditions) is not robust and only works under a narrow range of conditions (Morar, 2010). As a final conclusion, author state that machine vision measurements increase the understanding of the relationship between physical froth surface descriptors, froth phase stability and flotation performance, but the better understanding of froth phase behavior is required for the interpretation of the relationships between physical machine vision measurements and flotation performance (Morar, 2010). Fig. 13. Automatic segmentation of froth image (Wright, 1999).

found that the froth velocity can be well controlled by changing the air flow rate, while the color measurement and its control by changing the pulp level is not suitable for industrial implementation. Morar (2010) has presented detailed study about using of machine vision for evaluation of froth phase performance in flotation system. The testing was performed at two flotation plants having different mineral hydrophobicities – copper plant (highly hydrophobic minerals) and platinum plant (less hydrophobic minerals). Within the research, two types of analysis were performed: (1) Analysis of the effect of operating variables (air rate, froth height, frother concentration) on the measured froth variables (solids loading, bubble size, burst rate, froth velocity) and flotation performance (solids recovery rate and concentrate grade). (2) Analysis of the effect of measured froth variables on flotation performance. A part of the results obtained during this research is presented in Table 5. This table shows a summary of the relationships

5.2.3. Commercial application of Machine Vision Table 6 gives a review of contemporary machine vision-based commercial software for froth analysis. Although different in many aspects, all these software packages share the same idea – machine vision. Some of these examples are explored in more detail in the following pages. The successful utilization of one of these commercial systems was described in detail by Supomo et al. (2008). The control software VisioFroth, was produced by Metso Minerals Company, and installed in the PT Freeport Indonesia mine for the purpose of mass pull control of the rougher copper concentrate. Cameras were mounted on individual cells, measuring the froth velocity in order to determine the mass pull. For the measured velocity value, froth depth in the cell was adjusted, in order to achieve the desired velocity of the froth and, consequently, the desired mass pull. According to their report, utilization of software resulted in an immediate increase in copper recovery in rougher concentrate. Cipriano et al. (1998) presented a rule-based expert system that included the machine vision subsystem for the rougher flotation circuit at the copper mine in Chile. The supervisory control structure (commercially available as Aceflot) is able to identify froth

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Table 5 Determining the operating variable changes required to effect a consistent increase in the solids recovery and concentrate grade in a flotation cell [adapted from Morar (2010)]. Solids conditions

High hydrophobicity High concentration of solids (Copper rougher 1) High hydrophobicity Low concentration of solids (Copper rougher 3) Low hydrophobicity High concentration of solids (Platinum rougher 1) Low hydrophobicity Low concentration of solids (Platinum rougher 3) a

Solids recovery

Concentrate grade

Stability factor

Operating variable

" Bubble size ; Solids loading – " Burst rate " Solids loading ; Bubble size – " Burst rate " Solids loading – ; Burst rate " Bubble size –

"AR, "FH "FH, "AR, ;AR, ;FH "AR, "AR, ;AR, "AR, ;FH, "FH, "FH,

a

"FH ;FC ;FC ;FH ;FH, ;FH, "FH, ;FH, "FC ;FC "FC

"FC "FC ;FC, "AC "FC, ;AC

Stability factor

Operating variable

" Solids loading " Bubble size – " Burst rate ; Solids loading " Bubble size – " Burst rate " Bubble size – " Burst rate " Solids loading ; Bubble size –

;AR, "AR, "AR, "AR, "AR, "FH "FH, "AR, "AR, "AR "FH, "FH, "FH, "FC

;FH "FH "FC ;FC "FH "FC ;FH, "FC "AC ;FC ;FC, "AC "FC

AR – Air rate; FH – Froth height; FC – Frother concentration; AC – Activator.

Table 6 Machine vision based commercial froth analysis software. System name

Country

Plant, Ore

Dictuc S.A. (Chile) IMSOC (Sweden) Stonethree (South Africa) Outotec (Finland)

5. 6. 7.

Aceflot Froth Image Analyser Froth Sensor Frothmaster Advanced Control Tool (ACT) / FrothSense JK FrothCam OPTVision Froth SmartFroth

Copper, (Chile) Pyhäsalmi, Copper/Zinc, (Finland) Amplats, Mogalakwena, PGM, (S. Africa) Cadia Hills, Gold, (Australia), Macraes, Gold, (New Zealand), Alumbrera Copper/Gold (Argentina), Newcrest, Gold, (Australia) Porgera Gold Mine (Papua New Guinea), Peak Downs, Saraji, (Australia), Escondida, (Chile) Timbopeba, Fabrica Nova, Iron, (Brazil) Kennecott Utah, Copper, (USA)

8. 9. 10. 11.

VisioFroth Canty ECS/Process Expert FrothVision ITS Flotation visualization package

12. 13. 14. 15.

MetCam Plant Vision Tempotrack WipFroth

1. 2. 3. 4.

JKMRC (Australia) Cemi, (Brazil) University of Cape Town (S. Africa) Metso-CISA (Finland) JM Canty (USA) FL Smidth (Denmark) Manchester (United Kingdom) SBS, (Switzerland) KnowledgeScape (USA) Bluecube (South Africa) WipWare

PT Freeport, Copper/Gold, (Indonesia) – – – – – –

characteristics (e.g. color, number, size, shapes and stability of the bubbles, froth speed, etc.) and then suggest a course of action for the operator. Holtham and Nguyen (2002) have described the implementation of another commercial system based on the machine’s vision. The authors claim that the installed JKFrothCam system (developed and produced by JKMRC, Australia) significantly contributes to the improvements of coal flotation plant performances. Hyötyniemi et al. (2000) have described the system for visual analysis that was installed at the Cu-Zn flotation plant in Pyhäsalmi, Finland, and then integrated with the closed control loop system. The authors have categorized the following five froth variables as the most important: bubble collapse rate, transparency of bubbles, bubble size, red color intensity, and froth speed. Manipulated variable was CuSO4 dosage. Control action within the rulebased control algorithm (Table 7) was very simple: the flow of CuSO4 is either increased (‘‘+’’) or decreased (‘‘’’) by a fixed amount. The system was able to successfully prevent the froth collapse. However, when ‘‘stripped down’’, the intelligent or ‘‘AI’’ system can be somewhat easily brought down to the set of ‘‘If-Then’’ rules, thus raising the question of the ‘‘intelligent’’ component (Hyötyniemi et al., 2000). As a continuation of this research, Kaartinen et al. (2006) have described the operation of this system based on the combination of the subsystems of multiple cameras and expert controllers,

Table 7 Rule base at the Pyhäsalmi flotation plant (Hyötyniemi et al., 2000). Ranking

Condition

Action

1. 2. 3. 4. 5. 6.

IF froth thickness < lower limit IF BCR < lower limit OR bubble transparency < lower limit IF zinc content in rougher tailing > upper limit IF zinc content in scavenger tailing > upper limit IF froth thickness > upper limit IF BCR OR bubble transparency OR bubble size > upper limit ELSE

  + + + +

7.



which were previously installed at the Pyhäsalmi Mine, in Finland. The authors averred that the installation of this system helped contribute to the important improvements of zinc concentrate recovery (by 1.3% overall). Moreover, there was a strong correlation between the image variables and concentrate/tailing grades. The improvement of flotation process control–through the use of froth image analysis-based information–was also investigated by Saghatoleslami et al. (2004), Citir et al. (2004), Bartolacci et al. (2006), Lin et al. (2008), Yang et al. (2009), Kaartinen (2009), Marais and Aldrich (2011a), Morar et al. (2012), Liu et al. (2013), Cao et al. (2013), Kistner et al. (2013) Uusi-Hallila (2014). Table 8 shows literature review of machine vision application according to specific ore type.

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242 Table 8 Machine vision strategies according to ore type. Type of ore

Reference

Lead/Zinc

Bonifazi et al. (2001), Liu et al. (2005), Liu and MacGregor (2008), Hyötyniemi et al. (2000), Kaartinen et al. (2006), Bartolacci et al. (2006), Kaartinen (2009) Supomo et al. (2008), Moolman et al. (1995a, 1995b, 1995c), Morar (2010), Cipriano et al. (1998), Forbes (2007), Saghatoleslami et al. (2004), Morar et al. (2012), Liu et al. (2013), Uusi-Hallila (2014) Forbes (2007), Van Schaikwyk (2002), Wright (1999), Morar (2010), Marais (2010), Marais and Aldrich (2011a, 2011b), Morar et al. (2012), Kistner et al. (2013) Sadr-Kazemi and Cilliers (1997), Holtham and Nguyen (2002), Citir et al. (2004) Gui et al. (2013), Xu et al. (2012), Yang et al. (2009), Cao et al. (2013) He et al. (2013) Forbes (2007) Lin et al. (2008)

Copper/copper–gold

Platinum group

Coal Bauxite Sulfur Molybdenum Phosphates

According to available literature, there are a lot of reports about successful or partially successful application of machine vision systems in flotation, especially for PGM, zinc and copper minerals. However, their overall success in the industrial framework is still an open question. For example, discussing about machine vision systems efficiency Marais and Aldrich (2011b) assert: ‘‘Despite their potential benefits as online sensors, computer vision monitoring of froth flotation systems has not yet had a significant impact on the automatic control of flotation plants. This is particularly so in the platinum flotation industry. In these particular industries, the relationship between froth image features and the key performance variables of the flotation systems, i.e. recoveries and grades, has not been well established. This is seen as a major hurdle in the development of advanced control systems. Unlike the base metal industries, where the color of the froth or textural image features can give an indication of the loading of valuable material, such as bauxite, copper, zinc, and other base metals, platinum froths appear to be more difficult to interpret, since froth color at least does not appear to yield any indication of the loading of the froth.’’ (Marais and Aldrich, 2011b). However, we can say that fully efficient application of machine vision systems in other flotation plants (for concentration of copper, zinc, etc.) is not well established, too. For instance, Forbes (2007) also discuss about the slow implementation of machine vision systems for flotation froth analysis into the minerals processing industry. He emphasizes that ‘‘flotation froth machine vision research projects have been performed using video footage from only one industrial operation, with a limited range of operating conditions (often the operating conditions used are extreme conditions that do not generally occur under normal operation). This has resulted in systems which work under the specific conditions on which they were designed, but do not work well when used on other concentrators.’’ (Forbes, 2007). Also, one of the possible reasons is that research does not take into account all of the significant parameters affecting the froth features. Moolman et al. (1996b) reported about a series of variables than may cause differences in froth appearance. Some of them are very rarely taken into consideration (such as turbulence of the surrounding pulp, ionic strength, particle size, etc.). Experience has shown that although all froth flotation processes are using the same underlying principles, there are major differences in the characteristics of the froths at different plants. This is generally the result of processing different ore bodies, but is also affected by site specific operating conditions. The result is that numerous studies which have shown how concentrate grade can

be predicted over short time frames have not been extended to permanent industrial installations, or to other sites (Forbes, 2007). 5.3. Control methods beyond rules of classical logic As it is aforementioned, intelligent methods are being increasingly utilized in flotation control. Table 9 presents advantages and disadvantages of commonly used AI methods, which may be a guideline for the selection of appropriate flotation control technique. Several research-based publishings – as found with the artificial neural networks – advocates the hypothesis that artificial neural networks can be suitable methods for interpretation and classification of froth images in flotation systems, obtained by machine vision. Moolman et al. (1995a, 1995b) have demonstrated that it is possible to identify diverse froth structures with a high accuracy through the use of artificial neural networks, i.e. by LVQ (Learning Vector Quantization) algorithm. Furthermore, the authors state that this capability of recognition (or classification) of the important froth structures can be implemented in the on-line control systems, or by off-line operational procedures in a different manner. For example, the output variable from the classifier (i.e. certain class of froth) can be further included in the simple knowledgebased control system that could then identify the appropriate control measures to be taken. Such a control system would serve to support decision makers in the flotation plant. Marais and Aldrich (2011a, 2011b) have suggested a control procedure for the estimation of platinum flotation grade from froth image features, by means of artificial neural networks. One of the strategies used for extraction and analysis of flotation plant data is the inductive decision trees. In the study related with the application of random forests in mineral processing circuits control, Auret and Aldrich (2012) have concluded that forest regression models can successfully be used in presenting the nonlinear dependence of responses on independent variables, with the added benefit of not requiring user-input in terms of pre-specified interaction terms and nonlinear transformations, thus proving an advantage over conventional regression. Furthermore, random forest shows success in terms of efficiently detecting fault conditions in flotation circuits (Auret and Aldrich, 2011). Marais (2010) also shown that data analysis from captured images can result in reliable platinum froth grade predictions using random forest method. Aldrich et al. (1997) tested and compared the back-propagation method (artificial neural network training) with two probabilistic decision tree methods under industrial conditions. It was established that all the techniques were suitable for a knowledge-based control system that has the ability to replace human expertise in the area of flotation froth characterization. These authors also asserted that the secondary advantage of these inductive decision trees is their transparency and generation of rules, which can easily be understood by the operator, while the main or primary advantage of artificial neural networks is their easy adaptability within the expert system shell. By analogy, Gouws and Aldrich (1996) tested the suitability of inductive decision trees and genetic algorithms for the development of knowledge-based systems for the monitoring and control of the flotation concentration process. Inductive and genetic algorithms were used for the classification of different froth structures in the copper and platinum industrial flotation plants, as well as classification of P2O5 recovery from the phosphate flotation plant. It was determined that both algorithms were capable of classifying data as well as their human expert counterpart Genetic algorithms presented better performance than the inductive, however, further improvements were necessary before optimal results could be obtained. The classification rules obtained by both algorithms can thus be easily incorporated into the flotation plant decision

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Table 9 Advantages and disadvantages of AI methods [adapted from Xu (2011), www.academia.edu, Man et al. (1996), Gayathri and Malathi (2013), Nagendra et al. (1996), Gao et al. (2009), Horning (2010)]. Advantages

Disadvantages

Artificial neural networks (multilayer perceptron) Can perform tasks that a linear program cannot When an element of the neural network fails, it can continue without any problem by their parallel nature A neural network learns and does not need to be reprogrammed It can be implemented in wide range of applications and without significant problems

The neural network needs training to operate The architecture of a neural network is different from the architecture of microprocessors; therefore needs to be emulated – –

Fuzzy logic Universal function approximators – (a) given enough rules, a fuzzy system can approximate any function to any degree of precision; (b)number of rules required smaller than crisp rule based function approximator Parallel execution of rules – (a) output calculated once at the end of cycle; (b) rules are evaluated in parallel; (c) order does not matter; (d) no need for execution selection methods Modularity – (a) rules can be added and removed as needed; (b) eases development; (c) add as necessary to improve performance; (d) remove redundant rules to improve execution speed; (e) optimize individual rules Uncertainty – (a) rules can fire even if all antecedents do not match; (b) can deal with inexact concepts; (c) each rule corresponds to a wider range of input values Comprehensibility – (a) well crafted fuzzy rules are easy to understand; (b) makes a fuzzy expert system a ‘‘white box’’ Genetic algorithms Able to solve every optimisation problem which can be described with the chromosome encoding Very easy to understand and, practically, does not demand good knowledge of mathematics Execution technique is not dependent on the error surface, therefore multidimensional, non-differential, non-continuous, and even non-parametrical problems can be solved Have the possibility to solve the solution structure and solution parameter problems at the same time

Able to solve problems with multiple solutions Easy to transfer to existing simulations and models Decision trees (random forests) For many data sets, it produces a highly accurate classifier It handles a very large number of input variables It estimates the importance of variables in determining classification It generates an internal unbiased estimate of the generalization error as the forest building progresses It includes a good method for estimating missing data and maintains accuracy when proportion of the data is missing It can balance error in the class population of unbalanced data sets

making support system for operators. Accordingly, the same authors have developed the system for decision making support based on fuzzy logic rules for the purpose of industrial flotation plant control (Fig. 14). The rules were obtained by probabilistic induction, based on flotation froth features and physical process parameters as the input variables, and the flotation concentrate grade as the classification output parameter. The rules were fuzzyfied and incorporated into a fuzzy logic expert system shell. According to the results presented, the system is capable of on-line accurate prediction of flotation plant performances (Aldrich et al., 2000). The fuzzy logic approach to process modeling holds significant weight in the creation of expert systems for flotation circuit control. The computer integrated system for decision-making support and control in mineral processing plants was suggested by Miljanovic´ (2008a). The model integrates seven fuzzyfied monitoring and control levels: ore production (working sites/mines);

Computational cost – (a) more computations involved: fuzzification, fuzzy operators, composition of output fuzzy set, defuzzification; (b) complex membership function can aggravate this problem Defining the rules – (a) where do the rules come from; (b) major problem with rulebased systems; (c) need to get enough rules to be accurate; (d) rules need to be expressive; (e) rules need to be accurate Optimisation – (a) a change in the membership function can require a change in the rules; (b) a change in the rules can require a change in the membership function; (c) each parameter/choice effects the others; (d) multi-parameter optimisation problem –

–

Certain optimisation problems cannot be solved due to poorly known fitness functions which generate bad chromosome blocks in spite of the fact that only good chromosome blocks cross-over There is no absolute assurance that a genetic algorithm will find a global optimum. It happens very often when the populations have a lot of subjects Cannot assure constant optimisation response times. Even more, the difference between the shortest and the longest optimisation response time is much larger than with conventional gradient methods Applications in controls which are performed in real time are limited because of random solutions and convergence. This means that the entire population is improving, but this could not be said for an individual within this population. Therefore genetic algorithms should be tested first on a simulation model Usually require very large computational costs – Due to the way regression trees are constructed it is not possible to predict beyond the range of the response values in the training data Random forests tends to overestimate the low values and underestimate the high values – – – –

Classification Tree Induction

Rule Generation

Rule Fuzzification and Optimization

Heuristic Rules from Domain Expert

Rule Base Creation

Decision Support System

Fig. 14. Methodology of the development of the fuzzy rule based decision making system (Aldrich et al., 2000).

homogenization; production process of the mineral processing complex (including the flotation concentration); executive monitoring, decision-making and control; active analysis of the process;

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operative monitoring and decision-making; business monitoring and decision-making (Fig. 15). According to the author, a control algorithm is necessary for the establishment of the proposed fuzzy control model. The algorithm should produce the arranged set of fuzzy instructions, providing-upon execution-the approximate solution of the problem in focus. The fuzzyfication of the control model layers is fully justified because of the real system complexity, a lack of precise mathematical description of the real system process phases, dynamics of the process, changeability of the process features and the external influences and, in general, the non-linearity of the criteria control function. The construction of the fuzzy reasoning within the model also consists of defuzzification, i.e. the

MC level

VI

V

1. Optimal guidance of the mineral processing process (at the subsystem and system level). 2. Efficient corrective reaction to the potential disturbance effects in the real system. 3. Increasing the operational efficiency at the sub-system and system level. 4. Easier harmonization of the sub-system operation.

Environment

Fuzzyfication

EXTENDED TIME PROCESSES

VII

conversion of fuzzy conclusions into a crisp (numerical) value, since the control signal sent to the control subject must be a determined discreet value. The seven-staged fuzzyfied layered structure of the proposed model is creating the environment for:

Central management

Business monitoring and decision making

Operative management

Operative monitoring and decision making

Active process analysis

Specialized services

Intelligent system for decision making support

Database

Database Management System

Model base

Knowledge base

Inference System

Model Base Management System

Executive monitoring, decision making and control

Task

USER INTERFACE

IV

III

REAL TIME PROCESSES

SCADA Decision maker

Process Control

Measurements, process control, data acquisition, actuators

MINERAL PROCESSING OPERATIONS

Ore quality measurements

II

Ore homogenisation

Homogenisation

Ore quality measurements I M1

M2

M3

Mn

Mining production

Fig. 15. The model of computer integrated system for support to decision making and control in mineral processing (Miljanovic´, 2008a).

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5. Issuing an efficient prognosis and condition diagnostics, process trends and disturbances. 6. Establishing the more efficient control of operational costs. 7. Easier fulfillment of numerous environmental demands, etc. Within the intelligent decision-making support system (the 4th level of the control system presented in Fig. 10), the same author proposed the theoretical fuzzy model of the zinc mineral flotation concentration process at the Rudnik Mine in Serbia. The tests confirmed the positive correlation between the model data and the plant values of zinc recovery in the concentrate (Miljanovic´, 2008a). Osorio et al. (1999) have developed and compared three control algorithms (i.e. the classic expert algorithm, fuzzy algorithm and the fuzzy/expert algorithm) for the plant with the three banks of rougher flotation and a single stage of cleaning. The aim of the classic expert algorithm was to maintain the desired concentrate grade above certain minimal value, and the general tailing grade below certain maximal value. The operative goal of the plant is defined by operational zones (9 zones in total) established according to the concentrate grade – tailing grade diagram. In order to meet the requested demands, pulp level manipulation was performed according to the defined protocol. The heuristic approach to the plant operation did not consider the manipulation of other variables, such as air flow-rate or reagent addition rates. The fuzzy algorithm was developed by combining the knowledge base of the classic expert algorithm and the fuzzy logic principles in order to identify the zones and control calculations. The authors claim that by incorporating the laws of fuzzy logic into the algorithm, the following features are achieved: smoother transition between adjacent zones, improvement of uncertainty handling associated with zone definition, cross impact treatment of a manipulated variable between adjacent zones and simplification of the interaction between the operator and the control system. The fuzzy/expert algorithm is created as an attempt to combine the main advantages of the other two algorithms. The analyses have shown that the classic expert algorithm is the most robust, and its application resulted in the highest recovery. The fuzzy algorithm kept the plant operating inside the predefined ranges much longer than it did with the other algorithms, and with almost identical recovery as with the classic algorithm. The fuzzy algorithm is also proven to be flexible and intuitive to tune. Its main disadvantage is the generation of strong control actions. From almost all points of view, the combined algorithm had somewhat poorer performances than the other two, but it provided the smoothest control actions, from the practical point of view. Poirier and Meech (1993) developed a real time expert system for the purpose of copper flotation circuit control through the use of fuzzy logic rules. The system participates in detection of the significant variations of the values (increasing or decreasing) of process variables. When such variation is detected, the message

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is transmitted to an operator, which indicates the trend and the appropriate time interval of the variation. The system is capable of eliminating the exceptions, such as equipment failure. Rojas et al. (2009) have described two multivariable expert fuzzy logic control strategies used in the copper flotation rougher circuit. The first strategy is based solely on the measurements of the tailing and concentrate grades, while the second includes the measurements of the intermediary cell product grades. Simulation results have shown that by applying these strategies, the recovery can increase up to 2.5% when compared to the fixed control strategy. Although today, the intelligent control is mostly related to the artificial intelligence methods as well as multivalued logic practical realizations, some researchers still suggest using expert systems which are based on the rules of classic logic. For example, two expert algorithms presented by Pérez-Correa et al. (1998) for the rougher and cleaner flotation circuits are based on the classic logic rules. One of these algorithms was developed together with the plant operators and engineers with the aim of maintaining the copper content in concentrate, above the minimum, and the copper content in tailings, below the maximum. Process control was achieved by manipulation of the pulp level in cells. The second algorithm was designed especially to avoid control saturations. It was shown that these expert algorithms were capable of maintaining the plant operation within the defined operative zone (on a concentrate grade/tailing grade diagram) for a certain time period without the control saturations. However, in the paper published by Osorio et al. (1999) (this control strategy was described earlier in the paper) it was shown that fuzzy algorithm was more suitable, for longer periods of time, in maintaining the plant operation within the predefined limits. Commercial application of systems based on the soft computing methods is mostly related with the software already discussed in previous chapters (such as FloatStar Reagent Optimiser, for example). In the group of systems using fuzzy logic and neural networks, there are also Optimizing Control System (OCS) developed by Metso and Expert Optimiser, developed by ABB group. OCS is an expert control system based on fuzzy rules that incorporates image processing, virtual sensors, statistical models, neural networks, optimization modules and adaptive predictive models. Expert Optimizer has been developed that incorporates first principle modeling, expert system, fuzzy logic and neural networks to generate the optimal grade–recovery curve, improve concentrate quality, stabilize flows and minimize reagent use. (Cipriano, 2010). Table 10 shows a summary of flotation control strategies including artificial intelligence, according to ore type. Based on our findings, an important step forward is made in the field of flotation control regarding artificial intelligence (especially neural networks). However, we can conclude that the techniques based on artificial intelligence are still in the early stage of development and that their expansion should be expected in the future.

Table 10 Artificial intelligence based strategies according to ore type. Type of ore

Artificial neural networks

Fuzzy logic

Inductive decision trees/Genetic algorithms

Lead/Zinc Copper/ copper– gold Platinum group Phosphates Coal

Kaartinen (2009) Moolman et al. (1995a, 1995b), Aldrich et al. (1997)

Miljanovic´ (2008a, b) Osorio et al. (1999), Poirier and Meech (1993), Rojas et al. (2009)

– Gouws and Aldrich (1996), Aldrich et al. (1997)

Marais (2010), Marais and Aldrich (2011a, 2011b), Aldrich et al. (1997) Al-Thyabat (2008) Xiaoping and Aldrich (2013)

Aldrich et al. (2000), Muller et al. (2010)

Marais (2010), Gouws and Aldrich (1996) Aldrich et al. (1997) Gouws and Aldrich (1996) Auret and Aldrich (2011)

– –

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6. Summary discussion about the control methods After all the examples and various system features presented here, the sound question to be asked is which system is the best, or perhaps which system holds the highest potential for future development. The state of the art in an industry-related area is not always depicting the present research trends, but it can be a definite sign of things to come. It seems that for now, the model predictive control is the field leader. According to Villar et al. (2010) MPC is, by far, the most widely accepted multivariable control algorithm used in the process industry. However, knowing the development dynamics, we can infer that this will not be the case for the next decade. Perhaps we could consider the trends in the area of column flotation to be indicative, and to conclude that the intelligent control will be the main future playground but the rate of acceptance is not reassuring this conclusion. There are still major applicability questions to be resolved regarding fuzzy logic, neural networks or genetic algorithms. Table 11 summarizes findings regarding control methods or concepts presented in this paper. The categories are intentionally descriptive, since it is could be questionable to quantify some of the control concept aspects. Several other features can be considered when discussing this issue, such as costs, promptness, quantity of necessary data, etc. However, we feel that these six features and type of ore where it is predominantly used are the most important when considering implementation of one of these techniques. There are still a few remarks to be made regarding the comparison: (1) The consideration of conventional methods is purely for the purpose of historical contrast, since these methods are now usable only at lower hierarchy levels, (2) The method that it is most commonly used today is the best described, with its advantages and disadvantages listed extensively, and (3) The selection of examples is narrowed to metal sulfide ores only, since the examples on other ore types are scarce. An important issue to be discussed is off course the introduction of novelty systems at flotation plants, is its acceptance by the plant personnel. According to Almond et al. (2013) significant developments in plant automation and information systems have been made over the last decade. Automation technologies, solutions, and concepts that existed, but were considered risky or unreliable prior to the year 2000, had eventually evolved and gained acceptance. The authors have cited a confidential survey that was conducted by FLSmidth, and which resulted in the classification of personnel according to their relation to the automation systems and their implementation: Strategists, Implementers and Adopters with seven major areas of interest: Operational challenges, Knowledge of automation and strategy, Current automation practice, Benefits obtained, Appreciation and future plans, Approach, rationale and criteria for investment and Strategy adaptation (Almond et al., 2013). Certainly, this must be taken into account when considering between alternatives for control method introduction. Another positive experience about acceptance of an expert system by personnel employed in flotation plant is documented by Karhu et al. (1992). They reported that ‘‘The system has helped the operating personnel to run the process in a more stable and profitable manner, by providing instructions when important

events and critical process conditions occur. The operational staff is enthusiastic about the system and convinced of its usefulness.’’ However, there are also reports about difficulties related to the operation of control systems in flotation plants. For instance, Li et al. (2011) presented a field study from a human factors perspective to investigate the current status of control room operators and to explore the underlying ‘‘barriers’’ in their work environment. Study involved operators working at two different types of Australian mineral processing plants. Multiple data collection methods including control room observations, interviews, surveys and reviews of documentation were used. The findings revealed several serious shortcomings in the integration of people and technology in the current control room environment. Operator control of the systems was typically passive, alarms were mistrusted or ignored, and much technology was distrusted, rejected or not fully understood. According to the authors’ opinion, the main reasons for this were that the current information representation in the control room did not support the needs of human supervisory control and that various organizational issues such as insufficient operator training, poor shift handover and inappropriate task allocations significantly worsened the situation. However, they finally conclude that enhancing operator capacity is a promising new area for the mineral processing industry. Developing effective Human machine interfaces (HMI) and alarms, improving operator training, and optimizing organizational factors are all recommended as key items to help achieve a better integration of operators and technologies (Li et al., 2011). It is important to note that control methods depicted here, even if proven optimal for the particular plant, cannot be recommended (or even classified) for their use with different ores or types of flotation process. Therefore, it would be the most reasonable to conclude that achieving the ‘‘comprehensive’’ solution, which implies developing a control strategy that can be used for all flotation plants with mechanical cells (even with minor or moderate modifications), is probably not yet possible. However, the assumption that the ultimate goal regarding flotation control could be perhaps impossible to reach, can lead to altering the overall strategy aspect. According to this paper authors’ opinion, the promising alternative is revitalization of the concept of empirical solutions (employing intelligent methods), although it is known that they are typically applicable on a very small number of plants, perhaps even only on a limited time interval. Yet even with this notion, and based on the published research within the field of flotation processes behavior, intelligent methods are increasing the overall flexibility of the existing approaches and solutions, and are contributing to the formulation of generalized conclusions. Also, it can be argued that the road of future or foreseeable developments is paved only by incremental improvements, based on previous knowledge accumulated over the years. For example, we are now building cells larger than ever and employing all the smart and intelligent technologies; we are simplifying the process by lowering the number of measurement points, etc. Therefore the discussion regarding the hybrid approach is not meaning that some multiple-headed control entities are going to be built, but just that expert knowledge based methods needs to find their place in existing schemes. The good example of this principle are the

Table 11 Comparison of the application of three control concepts in mechanical cells flotation.

Conventional Model Predictive Intelligent

Implementation

Optimization potential

Sensitivity to disturbances

Robustness

Maintenance

Preferred sulphide ore type

Easy Complex Complex

Low Moderate to High High

High Medium to high Low to medium

Low Medium High

Easy to medium Medium to difficult Easy to medium

Wide range Copper/PGM Zinc/copper/PGM

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machine vision systems, where relatively new approach is used as a supplement to the ‘‘old one’’. Such development and improvement in flotation control could be expected, since the last two decade proved to be tryouts for the new approach playground, but it was a necessary process and a step toward positioning of the new methods. 7. Conclusion Although significant progress has been made over the last two decades in the areas of process control, flotation plants and mechanical flotation cells, there are important questions still not finding a definitive answer:  Firstly, there is the question of selecting appropriate control strategy. A unique control strategy should encompass micro and macro levels of plant control, or, in other words, production and business segments of the overall process. Notwithstanding the attempts, the appropriate strategy has not yet been developed.  The consensus exists, among many researchers, that classical methods of flotation process control, based on the traditional PID controllers, are not suitable for the comprehensive control of dynamic systems with regards to flotation, except, in part for the lower hierarchy levels.  In the area of advanced control, model predictive techniques are, due to their nature, an active ingredient of many adaptive approaches (employing both classic and soft computing based models), with good potential for further improvements. It is possible to enhance performances of the flotation processes by applying MPC methods, but these improvements are mostly related to short periods of time. Main difficulties of their application are connected to development of a reliable explicit process model and maintaining system stability.  Intelligent methods are playing an important role in flotation process control, although none of the available variations is established that would completely satisfy all process control aspects. Significant improvements in this area are related to the technological breakthrough in video processing and computer-supported techniques, as well as the utilization of soft computing methods, with the expected (and resulting) increased flexibility in process control, along with other advantages. Finally, taking into account the accelerated development of control methods in the field of flotation process achieved through a variety of different approaches and new ideas, there is still plenty of room for further improvements in the three major areas: overall approach to the control strategy, improvements on the individual control components and finding a better fit for the practical use of methods that we call ‘‘advanced’’ or ‘‘contemporary’’. Acknowledgments This investigation was conducted under Project TR 33007 ‘‘Implementation of the modern technical, technological and ecological design solutions in the existing production systems of the Copper Mine Bor and Copper Mine Majdanpek’’ funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia. References Aldrich, C., Moolman, D.W., Gouws, F.S., Schmitz, G.P.J., 1997. Machine learning strategies for control of flotation plants. Control Eng. Practice 5 (2), 263–269.

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