Newcrest Mining in Australia is providing useful solutions grounded in Data Science and using machine learning to help extract gold from its mines.
Recently we attended the Unearthed Data Science event in Melbourne. Newcrest provided operating data for a number of its plants, with the aim that some of the teams attending could explain how they are exploiting machine learning.
One particular system caught our eye — the autoclaves. What’s an autoclave? Take a look at the photo above.
Newcrest extracts gold from ore at their Lihir Gold operation in Papua New Guinea. This ore is rich is sulphide minerals (sulfide if you’re American) such as iron pyrite (FeS2) (aka “Fool’s Gold”). Yes, there’s real gold among the fool’s stuff.
Sulphides inhibit the processing techniques used to extract gold from ores, so it’s ideal if you can get rid of them.
That’s where the autoclaves come in. An autoclave is a type of chemical reactor that provides the right physical and chemical conditions for certain chemical reactions to occur. From the image above, you can see it is a long cylindrical vessel divided into sections by internal walls called baffles. Each section has its own mixer to ensure good contact for the chemical reactions, and an entry point at the bottom to let in oxygen gas and steam.
In Newcrest’s case, they use autoclaves to oxidise the sulphur minerals of the ore using a combination of heat, pressure and oxygen. The ore is crushed and mixed with acids to form a slurry. It is this slurry that mixes with oxygen gas inside the autoclave.
The sulphide minerals chemically react with oxygen to form other compounds that can be easily removed. The remaining solids are much richer in gold than the raw ore, enabling easier leaching of gold downstream of the autoclaves.
Using air directly as an oxygen source isn’t suitable — air is mostly nitrogen which is inert and would slow down the reaction of oxygen and sulphides. This would require a much larger autoclave to do the same job.
Read the source article at Sustainable Data.