Design and mining simulations

Process diagrams for mining facilities are complex because of the interdependence of vast amounts of equipment and multiple recirculated flows. It might be difficult to predict the impact of ore composition variability and the production rate on the quality of the concentrate produced. That’s why it’s now common practice to use simulations to optimize mining facility performance to maximize efficiency and profitability.

Simulations are initially used during the design process and engineering of a mining facility. The designer uses specialized software to produce mass and energy balances. This software helps develop numerous process diagrams quickly and accurately, while incorporating many design and operational criteria. These are static balances describing the distribution of steady-state mass and energy. This software also makes it possible to study how flow variability spreads in a process diagram under different operating conditions.

Simulations also make it easier to set up the facility. Here, the interest lies in the specifics related to the start-up and the gradual increase of tonnage to reach nominal production. The transient behaviour of equipment then becomes greatly interesting. By its very nature, software specialized in static simulations cannot address this issue and leave room for software specialized in dynamic simulation. By modelling the transient behaviour of equipment, dynamic simulations help:

• Verify that the desired production can be achieved through a developed process diagram and sized equipment.
• Assess the impact of buffer tank capacities on overall concentrator availability, in case of an unplanned stoppage.
• Identify possible bottlenecks and other sources of congestion.
• Validate equipment start and stop sequences for each area of the facility.

Dynamic simulations can also serve as an interactive training tool for new operators. Simulators are designed to emulate process behaviour following a change in setpoints, operating conditions or chemical composition of the ore. Scenarios are developed using actual production data from plant information management systems, laboratory analyses and archived data. Operators can have an even more realistic experience if the simulator is connected to a human-machine interface. As a result, even before operating the process, new operators can understand and control the impact that setpoint changes and disruptions have on concentrate quality.

Finally, dynamic simulations help validate control strategies. A new control strategy’s performance and impact on concentrate production and quality can be studied through virtual test environments, justifying its implementation. Corrections can also be made to it before it’s even implemented, reducing onsite debugging efforts. Simulators can also serve to train operators on how to use the new strategy. This helps reduce production losses when the strategy is commissioned.

Unfortunately, no software can simulate all applications. The main challenge therefore lies in choosing the best simulator to accomplish specific objectives.

Throughout the years, a lot of software specialized in producing mass balances and in sizing equipment has been marketed. Some address issues specific to one type of metallurgical process, while others are more versatile.

Nevertheless, the accuracy of calculations and reliability of results produced by these software programs rests solely on the quality of the design criteria used:

• In scoping studies, typical or presumed design criteria are initially used to develop a multitude of preliminary mass balances, each of which is designed differently.
• During prefeasibility and feasibility studies, the selected mass balance is specified. To do this, presumed design criteria are validated using industrial references and metallurgical tests in the laboratory or from a pilot plant.
• During the detailed engineering, equipment behaviour and sizing in minimum, nominal and maximum production scenarios are emulated from reliable and accurate metallurgical tests.

Equipment that produces the best results is then selected for purchase. There are fewer commercial software programs that are specialized in dynamic simulation of metallurgical processes and therefore require further programming. The dynamic models used—empirical, phenomenological, deterministic, stochastic—are selected based on the complexity of the desired simulation. Thus, programming dynamic production scenarios might require a lot of time, effort and investment.

Moreover, validating emulated transient behaviours can be challenging, given the complexity of implemented dynamic models. In any case, a compromise study must be performed between investment costs and economic benefits.

Nevertheless, simulations will now be an integral part of the mining design, optimizing, automation, economic assessment and decision-making process. An efficient application of simulations will increase a mining facility’s operability and safety. In a difficult economic context, where production costs are high, simulations can ultimately ensure the financial success of the mining project.