PROCESS CONTROL IN COMMINUTION

In a beneficiationplant, Process control is an essential component of any comminution system. The widespread adoption of automation in mineral processing plants began more than four decades ago, when rudimentary regulatory strategies for regulation of ore, water, and slurry were successfully deployed on single-loop analogcontrollers. Virtually all plants built today have a sophisticated digital control system that enables all basic control functions, providing the human–machine interface (HMI), and acting as the gateway to plant management information systems, which couple process and business controls. In addition, most new plants adopt advanced process control applications to deal with the multivariable nature of process optimization in real time. Although the journey has not always been smooth, the industry has increasingly embraced process control as one of the most capital-effective investments available in the pursuit of lower costs and increased revenues.

The need for process control is made evident in early stages of the comminution circuit design process. To illustrate, Figure1 shows a hypothetical distribution of a hardness index in an ore body.

The need to adapt to changing feed conditions is readily apparent. If the plant is designed to produce the desired product size for a hardness of 14 at design tonnage, the ore will be harder 17% of the time; unless the tonnage is reduced, the product size will be too coarse and possibly some loss in liberation will occur. On the other hand, 83% of the time, the ore will be a softer, finer product, and more liberation will possibly result.

Fig1. Cumulative probability density function for ore hardness from testing

The key implicit assumption here is that a process control system will allow the circuit to achieve steady-state targets and overall operational stability in the face of feed-ore variations.

These temporal changes in ore characteristics in the feed to a comminution process are called “disturbances” because nothing can be done by the operator or control system to modulate them. The nature (frequency and amplitude) of these disturbances will dictate the severity of the control problem and the complexity of the solution. For example, Figure 2 illustrates three possible variants of circuit feed for the ore characterized in Figure 1. Case A illustrates a well-blended feed; case Bshows greater short-term variability; and case C shows longer-term variability. As Murphy’s law would correctly predict, disturbances, such as those in cases B and C, are more common in practice. In fact, all of these disturbances coexist.

Fig 2. Temporal variation of ore hardness

Disturbances will arise in ore hardness (e.g., different ore types and genesis modes), feed size (e.g., blasting practices and stockpile segregation), and liberation requirements (e.g., requiring a change in grind). It is also quite common to see disturbances arising from internal sources; for example, a sump pump that intermittently delivers a dilute slurry to a cyclone feed pump box, or a mechanical feeder prone to flow interruptions. Although the comminution process may well be stable to these fluctuations without intervention, control systems are normally required to ensure stability and to enhance the overall economic performance of the process.

To continue with the illustration above, Figure 3 A shows a hypothetical grind-size recovery curve. If we were able to maintain a target product size of 70% at 200 mesh, we would expect to see a valuable metal recovery of 88.6%. However, if the feed tonnage is constant, the grind will vary based on the nature of the disturbances. Combining the information of Figures 2 and 3A produces Figure 3B. As we would expect, the greater variability in hardness for case C produces a wider distribution in grind size to the separation circuit. Figure 3.45B also shows the overall recovery expected under each open-loop operating scenario. (Open loop implies no intervention by the operator or the control system.) Clearly, the more stable feed of case A provides a recovery much closer to the optimal value shown in Figure 3A, while case C incurs an -2% recovery loss.

Fig 3. Illustration of the impact of disturbances in comminution on downstream separation Processes

In this hypothetical case, if a control system were to be applied to maintain the grind at the average or target value, the total tonnage treated would be essentially the same, but a 2% recovery gain would be seen for case C. The latter number is more or less typical of the recovery gains associated with supervisory control applications. Throughput increases frequently lie in the range of 3% to 15%. The magnitude of these numbers underscores the attractiveness of such investments.

Industrial process control systems are powerful tools for maintaining process stability and ensuring optimum economic performance in the face of disturbances. The complexity of the control strategy depends in part on the complexity of the process  and in part on the nature of the disturbances. Estimates of the nature of disturbances are increasingly available at the design stage, opening new avenues for the a priori design of control strategies. Operationally, efforts to mitigate disturbances upstream (at the mine or crusher) will simplify the control requirements, although the spatial variability of ore characteristics often precludes effective blending.

The combination of the magnitude and frequency of the disturbances will also have an impact on

control requirements. Simply put, minor amplitude variations are easier to handle. Similarly, low-frequency disturbances can often be very effectively rejected by control, while the process effectively filters very high-frequency disturbances. Those lying in the intermediate range can usually be rejected to a greater degree (the shorter the frequency). This frequency range is related to the time constant of the process. For example, the dynamics of a response to a feed-hardness change in a grinding circuit are much slower than the change in water flow in a pipe to a response in supply pressure.

The Control Triad

The control triad illustrated in Figure 4 provides a useful framework for this overview. This schematic conveys the message that an effective process control system will include the proper blend of field instrumentation, hardware, and control strategies.

Fig 4. The control triad

Figure 5 provides a practical illustration of the control triad in the context of a simple water flow regulation loop. In this instance, the field instrumentation consists of the orifice meter and ball valve. The hardware comprises the input/output (I/O) subassembly, the computer with the basic software, and the HMI. The strategy employs a simple well-tuned Proportional Integral and Differential (PID) Actions control law, where the operator determines the set point or target for the water flow rate. Of course, there are many important and related subjects that are beyond the scope of this discussion. These include signal filtering, sampling intervals, loop tuning, and dead-time compensation.

Fig5. A simple flow control loop

Instrumentation

Because final control elements are largely restricted to devices that control position (e.g., valves, knife, or flop gates) or electrical motor speed (e.g., feeder, pump, and mill), this section will focus on sensors, offering a much broader range of devices. The first law of process control—“All control starts with measurement, and the quality of control can be no better than the quality of measurement,” —or in the vernacular—“Garbage in, garbage out”—helps validate this choice.

Table 1 lists some of the more common sensors used to monitor comminution circuit equipment. (Because there are many manufacturers of competing instruments, we have elected to distinguish instruments on the basis of the technology employed to make the measurement.) Although the list is not exhaustive, it does show that there is a good capacity for measurement in such process systems.

It is evident from this table that the process control system designer often faces a problem related to choices. In other words, what kind of technology is best suited for the measurement problem at hand, and, which vendors manufacture proven products employing this technology? In the lower level stabilizing loops typically associated with the regulation of ore, slurry, reagent, and water flows, the preferred sensors are generally well established. For example, electronic belt scales are the sensor of choice for measuring solids mass flow on a conveyor belt.

Instrumentation provides the interface between the process and the control strategies. The proper selection, installation, and maintenance of these field devices is essential to ensure that the benefits associated with process control applications are sustained for the life of the project. Ongoing sensor development efforts also means that the process control engineer needs to stay abreast of measurement technology, looking for opportunities to further develop or enhance the performance of a process control system.

Hardware

Control hardware most frequently encountered in mineral processing plants are the Distributed Control Systems (DCS) or the programmable logic controllers (PLC). In many plants hybrid architectures involving a combination of DCS and PLC technologies are common. Figure 6 is an illustration of such a hybrid structure and shows the hardware layout for a typical process control system. This picture is expected to change in the coming years as smart instruments and equipment displace the more traditional I/O interfaces. Moreover, as bandwidth increases, the likelihood of delivering control applications over the Internet increases, and remote hardware and application maintenance and development support will be simplified.

Fig6. Components of a distributed control system

To provide some notion of scale, the I/O count (i.e., the total number of discrete, analog, and digital inputs and outputs) will range from about 2,000 for simpler, smaller plants to 6,000 for larger, more complex operations.

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