Daniel led a business improvement project in Newcrest to reduce water usage in tailings. The Humyn.ai community produced hundreds of predictive models. The best of these was deployed in a solution that predicts tailings density three hours in advance, allowing Newcrest to optimize the process.
Newcrest Mining wanted to find a method to predict tailings underflow density in order to enable more efficient recycling of water in the tailings process.
In metals processing, large tanks are used to separate the mineral solids from the process water. The separation of water from tailings (mineral processing waste product) is a critical process as it enables the recycled water to be reused within the processing plant. Sustaining a high density of tailings underflow increases the amount of water that can be reused. This reduces the operating cost for ongoing processing of the ore and improves the environmental sustainability of the operation.
Participation in the project was truly global, attracting data scientists from countries such as Canada, India, USA, Argentina, China and South Africa. 150 highly skilled individuals formed teams who submitted over 750 predictive models.
Newcrest is realising significant financial savings through efficient recycling of water in the tailings process. This has also made a significant contribution to the environmental sustainability of the operation - saving billions of litres of water.
"We had 25 teams from over 10 countries competing to devise a method capable of predicting tailing underflow density three hours ahead of time. Newcrest is very proud to be leading the world in leveraging crowd sourcing through this innovative platform, solving highly complex business problems for the mining industry.”