That's a great question that cuts through the hype! You're right to ask about practical applications that work today, not just future promises. Here are some concrete ways machine learning is already enhancing PLC control loops:

1. **Predictive Maintenance**: ML algorithms analyze sensor data from motors, pumps, and other equipment to detect subtle patterns that signal impending failures. This happens in real-time on modern PLCs like the Finder Opta, which can run neural networks directly on the controller.

2. **Anomaly Detection**: Instead of just setting fixed alarm thresholds, ML models learn normal operating patterns and flag unusual behavior that might indicate quality issues or process deviations before they become critical.

3. **Adaptive Control Loops**: ML can optimize PID parameters in real-time based on changing process conditions. For example, if viscosity changes in a mixing process, the ML model can adjust control parameters to maintain optimal performance without manual intervention.

4. **Quality Prediction**: By analyzing process variables in real-time, ML models can predict product quality outcomes and make small adjustments to the control loop to maintain specifications.

5. **Energy Optimization**: ML algorithms can learn energy consumption patterns and optimize control sequences to reduce energy usage while maintaining production targets.

The key is that these applications don't replace traditional PLC logic - they enhance it by providing smarter decision-making capabilities that work alongside your existing control strategies. Modern PLCs with embedded ML capabilities make this practical today, not in some distant future.