Automating hot-end through physics-AI digital twins: can we increase yield while reducing costs?

The current structural crisis of our market is biting margins due to the combined pressure of energy costs, inflation and macrotrends in global drinking habits. Investments are frozen and production is being resized worldwide. However, although reducing production is a matter of short-term survival, the mid-to-long term life of the glass industry can only be sustained by structured optimization of how we make glass products.

Optimization at the Hot End can occur, e.g. (i) by improving glass conditioning in the forehearth while reducing energy consumption, reducing dependency on energy cost volatility; (ii) by reducing downtimes during job changes, improving flexibility in a market demanding make-to-order approach; (iii) by continuously controlling and adjusting the process, reducing defects in the final container; (iv) by improving predictability and inspectability of process and machinery, allowing issues to be prevented ahead of time.

Physics-AI Digital Twins enable this in a structured, safe and reliable way. We present evidence of this breakthrough from conditioning to blowing, proving double-digit impact.