Applied Computing

A purely data-driven model may give suggestions that are statistically reasonable but physically impossible, like operating parameters violating mass balance. Once engineers see such output, they never trust the system again. Adding physics constraints keeps the AI's suggestions at least within the physically feasible range — this is the watershed for whether it gets adopted.

The biggest difficulty is usually not the technology but the data — whether the control system's historical data is fully preserved, whether labeling is consistent, whether it can be connected across plants. Most old plants get stuck at this stage. Doing a data inventory first rather than jumping straight to vendor selection is advised.