About Digital Twins

Many people rave about “Digital Twins”. Like cars, performance and prices are widely ranged. Following is my “chat“ with ChatGPT

The key takeaway are:

1. The idea of a "complete mirror" is extremely ambitious — and often not fully achievable in practice. But digital twins can still be very valuable, even when they reflect only a portion of that complexity

2. A digital twin of a chemical plant is never perfect — but it doesn’t have to be. The goal is to capture the key dynamics that influence safety, efficiency, and performance, and adapt continuously.

3. One of the core challenges of maintaining useful digital twins in industrial environments: change over time — both intentional (upgrades, retrofits) and unintentional (wear, drift, degradation).

4. To ensure your digital twin evolves as the physical plant evolves:

• Enable real-time data ingestion

• Retrain or recalibrate models periodically

• Track asset configuration/version changes

• Integrate with maintenance and upgrade logs

• Use hybrid models (physics + AI) for robustness f. Set thresholds for human review or alerts on model drift.

• Read the full details here: