Development and validation of artificial intelligence models to predict standard comminution parameters
By Leonardo Lara
We have a new peer reviewed paper in Minerals Engineering showing how Deep Neural Networks predict standard comminution parameters directly from Geopyörä breakage test data. The result is higher accuracy with smaller samples and faster turnaround. What this means for you: - Less sample mass, more samples across the orebody, better variability capture - Direct prediction of Axb/DWI and BWI from force, energy, t10, SG and related features - A clear path to scale geomet prog
We have a new peer reviewed paper in Minerals Engineering showing how Deep Neural Networks predict standard comminution parameters directly from Geopyörä breakage test data. The result is higher accuracy with smaller samples and faster turnaround.
What this means for you:
- Less sample mass, more samples across the orebody, better variability capture
- Direct prediction of Axb/DWI and BWI from force, energy, t10, SG and related features
- A clear path to scale geomet programs with lower cost per decision
Authors:
Marcos de Paiva Bueno, Leonardo Ribeiro Lara, Thiago de Almeida, Malcolm Powell
Read the full paper: https://www.sciencedirect.com/science/article/pii/S0892687525006776
Or download it below: