Steady state simulation in mineral process assessment

Introduction

Understanding the behaviour of mineral processing operations is a constant battle, whether it is finding the right process configuration for the resource or getting an existing process working better. Ideally, we collect representative samples and undertake test work to explore our ideas. However, this is often a slow, inflexible process that doesn’t allow for innovative solutions to be easily examined. The other option is to build a mathematical model of the circuit and alter input variables to examine the effect. Known as process simulation this approach allows rapid investigation. It is not a replacement for test work but gives an opportunity to get more value from the physical work we do.

Process simulation is now a core tool in development and operation of mineral processing operations. The capability to model the behaviour of different ore domains through a variety of process configurations has greatly enhanced the flexibility of process design and improved efficiency. Ongoing improvements in understanding and modelling of unit operations mean that our confidence in process simulation is constantly improving.

Process simulation falls into two categories, steady state simulation and dynamic simulation. Steady state simulation is used to investigate the steady state behaviour of a system. Dynamic simulation takes this one step further and seeks to investigate the behaviour of a system at any given moment, even when not in steady state. You can think of steady state simulation as modelling the stable behaviour or the plant, whereas dynamic simulation allows for the fluctuations in input variables.

The drive for digital transformation and a more connected minerals operation has meant that focus in recent years has been on Dynamic Simulation and development of Digital Twins. This is especially useful for real time modelling and predictive control. However, steady state simulation still has powerful applications in process development and optimisation, allowing investigation of scenarios with comparatively less computing requirements.

In this article I will investigate the different options for steady state simulation, how it can be effectively used in process development and down the line in process optimisation. Finally, I will look at the value that can be generated from using simulation in smarter and more efficient applications. In future articles I’ll investigate dynamic simulation and the exciting opportunities around the use of Digital Twins in mineral processing.

Steady state simulation

There are a range of tools available for simulation of mineral processing circuits. Broadly, these tools offer a graphical interface to generate mass, water and energy balances. The response of unit operations is determined by either theoretical or empirical models built into the software, with most allowing for selection of the most appropriate model.

A selection of tools that I have experienced over the years are summarised in the table below. These all include models for mineral processing specific unit operations, differentiating them from software packages that focus on other chemical industries.

I won’t go into the pros and cons for each package in this article. Generally, I have found that they all have their strengths and points of frustration but do the job they were intended for.

A tool for development

HSC SIM example Source: https://www.outotec.com/products-and-services/technologies/digital-solutions/hsc-chemistry/hsc-sim-process-simulation-module/

For those of you that have gone through the project development process you have probably experienced the use of steady state simulation. Simulation provides a fast and reliable tool to generate mass, energy and water balances. This reliability is determined by the quality of ore characterisation and test work data. If representative samples have been used and test work well executed the results can feed into generation of process design criteria, simplifying inputs for engineering design.

When compared to the conventional development path a graphical simulation is much more flexible first step than a physical pilot plant, allowing multiple process configurations to be examined quickly. One of the most powerful applications of simulation, this means innovative configurations can be rapidly tested. Importantly, it allows physical test work to be focused on the most promising configurations improving efficiency and reducing test work costs. The results are reproducible allowing for investigation of the effects of changing variables on process behaviour.

Designing a process based on just a limited number of ore types has led to operational problems in many projects. A simulation model paired with a strong geometallurgical model can allow the behaviour of all identified metallurgical domains to be investigated. This can be extended to include outliers that may have big process impacts. Variability analysis doesn’t need to be restricted to ore variability. Investigating things like changing process water properties can reduce surprises down the line. In this manner simulation can be used to effectively reduce variability risk in a process.

Overall, a strong steady state simulation model can be used in process development for a huge range of investigatory tasks. It will never completely replace the requirement for test work but can be effective in reducing the amount of wasted test work completed and streamlining the development process.

A tool for process improvements

https://metsim.com/products/comminution-gravity-separation-module/

Simulation shouldn’t be restricted to process development. For operating circuits steady state simulation can provide a useful tool to rapidly investigate scenarios for process optimisation. Dynamic simulation may be more comprehensive but requires much more effort to develop. Often a steady state simulation model can be developed or updated quickly, giving an opportunity to look at a wider range of scenarios. The inputs for modelling and more constrained where operational data can be used to tune the models, giving more confidence in the results. Applications can include investigation of targeted unit operations or internal circuits. They can also be expanded to examine potential impacts of changes in one part of the circuit on the rest of the process, reducing risk associated with changing configurations.

Used well a steady state simulation model can be the first point of investigation for project metallurgists undertaking continuous improvement. Virtually reconfiguring the circuit in a steady state simulation model can allow us to examine scenarios that would be impossible to investigate physically. This allows them to be rapidly rejected or if positive have a strong business case built before significant expenditure on test work is required.

Generating value from simulation

SysCAD example Source: https://help.syscad.net/index.php?title=File:Example_Projects_-_Gold_Example.png

As we continue to plow forward in the Digital Age modelling and simulation will continue to gain importance in mineral processing. While it is realistic to expect that in the not too distant future our processing plants will be controlled by dynamic models it is important to remember that we can already gain significant value using simulation in day to day operation.

I recently completed work on the Feasibility Study for Aura Energy’s (ASX:AEE AIM:AURA) Tiris Uranium Project. In this study we used steady state simulation extensively to examine alternative process configurations and variability in feed material properties. This allowed us to focus test work on key design areas, while providing robust senstivity analysis to changing mineralogy. We were able to test a wide range of configurations, leading to several innovations that reduced the water and reagent consumption significantly. Because test work was focused on what mattered to design the budget was significantly reduced without compromising the quality of the results.

Overall, the use of steady state simulation early in a project opens a range of opportunities to explore options that may not be available otherwise. It leads to more robust projects, more efficient use of test work budgets and the opportunity to be creative without introducing additional risk.