The first step is the importation of 73 years of historical data. This data shall then be cleaned, transformed, and formatted to enable analysis, reporting, and machine learning modeling. An Al platform core algorithm is used to re-align the data to enable a better understanding of how different components like power and water usage and grade of contamination impact the production process.
Aligning the data with the business processes and advanced analytic techniques reveals that the features with the highest impact on the production rate are:
- Mill feed – the amount of material going into the silo
- Sifting screen – the speed of the sifting based on the desired separation of the material
- Amount of water in the R spiral – based on how the material is crushed, water needs to be adjusted
Statistical and machine learning techniques are used to recommend optimum settings and actions to steer yield in real-time and generate revenue.
