by Kathie McGregor

Using computer modelling to improve flash smelting inefficiencies

Since its commercial implementation in the 1950s, flash smelting has proven to be one of the cleanest and most efficient methods for producing copper and nickel.

The technology is well-established and there’s still a lot of life in flash smelting, but there are clear areas for improvement.

Some operations face risks when product quality or throughput changes significantly.

There’s also a risk if the plant becomes difficult to operate due to high maintenance costs or outdated processes.

Additionally, there are some smelters that have never been able to reach the specifications that were originally mapped out.

There’s also the possibility that a plant could appear to be running well, when in fact “human assessment” and plant observations may have provided extremely limited data that doesn’t accurately portray the full potential of operations.

With growth, productivity and environmental concerns all becoming stronger influences in the industry, the key challenge for smelters is to refine and optimise their flash smelting technology — which is no small feat, considering the high level of complexity involved.

But the development of solutions in response to certain performance-limiting pain points can bring about greater throughput and subsequent financial returns for smelting operations.

Potential areas for improvement within flash smelting

The gradual or rapid change in concentrate composition and feed blends can adversely affect furnace operation. These inefficiencies in flash smelting operations are occurring more often now that primary feedstocks are steadily being depleted.

One strategy being implemented to offset these changes is the blending of high and low-quality ores, often of greatly varying compositions.

Additionally, some smelters buy concentrate from a variety of sources and produce a blend of concentrates. This means that optimal performance can sometimes be changing rapidly (on a monthly, weekly or even daily basis).

As a result, smelting operations are forced to adapt their operation to accommodate the changing nature and rising complexity of feed to the smelter.

This change in process can cause operational issues to occur and manifest themselves in a number of different ways within the flash smelting plant:

• Poor design of the concentrate feeder — this can lead to poor combustion performance and the collection of unreacted solid feed floating on the bath surface.
• Inadequately designed furnace chemistry with too much or too little oxygen added to the furnace — this can cause over-reacted or under-reacted particles entering the bath, which in turn affects matte and slag properties and the ability of operators to maintain these high temperature liquids at an even temperature to maximise refractory life.
• Too much solid feed material in the off-gases — too much concentrate exiting the furnace through the uptake shaft can cause build-up of accretions in the throat between the uptake shaft and waste heat boiler. These accretions can significantly reduce the flow area of the throat and they are very difficult to remove, typically involving furnace downtime and operators working in dangerous spaces.
• Inappropriate cooling of process gases — this occurs in the waste heat boiler. It can lead to the condensation of sulphuric acid droplets which then impinge onto solid surfaces and corrode the plant.

If you’re operating a smelter that is suffering from any of these inefficiencies, you not only face reduced revenue, but also increased downtime and high maintenance costs as you try to keep up with change and growth.

The plant observations may only provide limited data as well, which can leave you open to the possibility of continually running at un-optimised levels.

These potential budget blow-outs, when accompanied by a drop in product quality or throughput can drastically affect the profitability of a smelting operation.

The barriers in the way of optimising the flash smelting process

It’s immensely challenging to optimise the flash smelting furnace operation because of the extreme difficulty of observing it experimentally.

Currently, adjustments to operating conditions can only be made through assaying the composition of matte and slag as they leave the furnace.

Then, where possible, the temperatures and composition of process gases are monitored.

This method isn’t entirely effective, due to:

• the time delay between changes to process conditions and observable changes in furnace operation
• the limited access available to take sufficient reliable measurements.

This makes close control of the furnace extremely difficult.

Using CFD modelling to overcome the difficulties of improvement and optimisation

In any of these situations, it’s possible to identify areas of improvement to throughput by using computer modelling.

Computer modelling of flash smelters can assist in identifying how to improve throughput — even in a plant that appears to be running well.

It can also guide design and operating strategies in ways that plant observations cannot.

Without modelling, operators are limited to only monitoring a small number of process variables that do not give details of local conditions.

Plant trials can be used to take, for example, temperature and gas composition samples in the smelter.

But these are costly and usually only provide limited information in a few locations.

Computational fluid dynamics (CFD) modelling is an advanced modelling technique that can solve the difficulties of understanding performance issues, guiding design changes and developing solutions to key inefficiencies that can arise.

The use of a computer model can allow detailed predictions of furnace operation under any specified condition.

It allows optimised conditions to be determined in a computer, and design changes to be evaluated before costly changes are made to the furnace.

Effective modelling of the furnace operation means that it’s far easier to achieve optimised throughput.

The consequent increase in product quality and productivity results in maximum revenue and a drastically reduced cost in repairs.

Access CFD modelling and optimised operations: partner with CSIRO

Our Minerals Process Optimisation team at CSIRO has expertise in creating custom-built CFD models of flash furnace reaction shafts, as well as flash furnace uptakes and waste heat boilers.

We also have the capability to create models of the furnace settler. The modelling can be done in a tightly controlled way without impacting smelter operations.

This allows predictions to be made in any given scenario of interest.

Such models can be used to:

• systematically vary process conditions or plant geometry
• closely observe operation of the plant under a range of different conditions
• identify conditions that produce optimal performance of the smelter.

Using CFD models to evaluate potential design and operational changes allows you to be proactive, and it means that the potential risks of such changes can be mitigated.

A number of potential options can be evaluated before you make the decision to undertake expensive or labour-intensive plant modifications.

As world leaders in the area of CFD modelling, we have a wide capability across the areas needed to simulate the process successfully. These include:

• pyrometallurgy
• physical modelling
• on-site measurements.

Our capabilities have been developed over our 35-year history of pyrometallurgical modelling, and the flash smelting model development is based on extensive in-house measurements pioneered by world leader in mineral chemistry, the late Dr Frank Jorgenson.

Additionally, we have capabilities for maximising the ROI of furnaces using Top Submerged Lances (TSL).

The CFD team develops models using the latest version of the ANSYS CFX software suite and has access to a range of high performance and super computing resources.

Contact the Minerals Process Optimisation team on +61 3 9545 8912 or email me, Kathie.Mcgregor@csiro.au to talk about how you can optimise your flash smelting process, improve understanding of its operation, and reach increased levels of throughput.

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