Context, Approach & Methodology

The mill operation lacked reliable predictive visibility. Operators had to react based on lagging indicators, leading to sudden overloads, emergency shutdowns, and increased wear. Energy consumption remained very high due to instability in mill loading.

We started by analyzing sensor data, energy profiles, operating modes, and historical overload events. Our team engineered features across vibration, torque, feed rate, ball-loading, and acoustic signatures to build a robust predictive dataset. A supervised ML model was trained to identify precursor patterns signaling overload conditions.

Once deployed, the model continuously monitored live streams and triggered operator alerts 30–40 minutes in advance through an integrated dashboard. We also optimized operating regimes by identifying ball-loading configurations that reduce overload probability.

The project combined data science, operations optimization, and real-time monitoring to deliver sustained performance improvements.

The Groundwork

1. Diagnostics & Modeling

• Historical overload profiling
• Feature extraction from mill sensors and acoustic patterns
• ML model development predicting overload windows
  

2. Real-Time Alerting

• Operator dashboard and alert mechanism
• Forward-looking warnings (30–40 minutes)
• Continuous performance tracking
  

3. Operating Optimization

• Optimization of mill loading parameters
• Recommended operating modes
• Lower energy intensity and smoother cycles

Analysis

The ML system surfaced hidden variable correlations previously impossible to detect manually. Overloads strongly correlated with specific amplitude behaviors in vibration signals and certain ball-loading ranges. Predictive insight enabled proactive load adjustments and optimized mill operating modes.

With the overload risk dramatically reduced, operators transitioned from firefighting to proactive cycle management — stabilizing output and reducing both energy intensity and mechanical stress.

Results

• 30% reduction in downtime
• Energy usage decreased from 530 kWh to 390–420 kWh
• Annual impact of $0.85M–$1.16M
• Improved mill stability and operator confidence
• More predictable and efficient production cycles
  

This case demonstrates the power of predictive ML in high-complexity industrial environments.

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