The challenge lies in the hydro cyclone's inaccurate classification, causing misplaced particles to recirculate, leading to metal losses. A predictive AI-based system for real-time P80 visibility is needed to optimize milling process parameters and enhance control.
The challenge is that the hydro cyclone's inaccurate classification, causing misplaced particles to recirculate in the milling circuit and leading to overgrinding of Pb. A solution is required to predict the P80 of the cyclone (based on input parameters like ball mill power, rod mill power, cyclone feed density and flow, cyclone overflow density and flow, mill fee water etc.) to enhance precision and control over output parameters.
The inaccurate classification of particles within the hydro cyclone results in the recirculation of misplaced particles, contributing to either overgrinding or under grinding of particles which ultimately leads to metal losses. The lack of control over the process and output parameters exacerbates the problem.
The system should be able to predict the P80 (possibly using AI capability), advising operators on input parameters. It should integrate easily with the existing plant configuration, and be user-friendly for operators