This project focuses on developing an integrated approach based on multi-sensor monitoring and artificial intelligence to enhance the monitoring, control, and optimization of ball mill performance. In this approach, data from vibration, acoustic, and thermal analyses, along with strain-gauge measurements, are analyzed in an integrated manner to monitor and assess the structural health and performance of the mill without relying on periodic shutdowns.
The proposed solution incorporates a Digital Twin and a closed-loop intelligent control system to enable the prediction of key parameters, real-time response to feed variations, and automated process control. The ultimate goal is to move from operator experience-based monitoring and adjustments toward intelligent, data-driven control, with a focus on increasing production capacity, improving efficiency, and reducing energy consumption.