Daniel C. reduced water usage and saved millions by predicting tailings density.
Daniel led a business improvement project 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 his company to optimize the process.
Impactful Challenge
The company wanted to find a method to predict tailings underflow density in order to enable more efficient recycling of water in the tailings process.
In processing, large tanks are used to separate solids from the process water. The separation of water from tailings (a 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 product and improves the environmental sustainability of the operation.
Global Talent
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.
Meaningful Results
The company is realizing 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 liters 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. [We] are very proud to be leading the world in leveraging crowd sourcing through this innovative platform, solving highly complex business problems.”