Intelligent process control system for predicting operating conditions of slag grinding machines: a data mining approach for improved efficiency and energy savings

Abstract

This study develops an intelligent data-driven approach for optimising slag grinding systems. Slag grinding exhibits complex nonlinear dynamics that challenge control. The proposed system monitors key operating parameters to assess machine health and automate control adjustments. Operating data are collected, and features linked to health status are identified using data mining techniques. Cluster analysis categorises historical data into healthy/unhealthy modes to build a condition library. Real-time data are then evaluated against this library. A predictive model forecasts future trend. The system was implemented in an industrial slag mill. Results demonstrated reduced vibration and energy use versus manual control. Validation confirmed improved accuracy in predicting mill responses. Significant energy savings were achieved annually through optimised control. The system enhances safety by automating adjustments while minimising costs and environmental impacts. Data-driven strategies overcome the limitations of traditional methods, representing an advance for intelligent management of industrial processes. Benefits were confirmed through rigorous factory implementation and performance monitoring.