CrystalGPT: Enhancing system-to-system transferability in
  crystallization prediction and control using time-series-transformers
Niranjan Sitapure Joseph S. Kwon
For prediction and real-time control tasks, machine-learning (ML)-based
digital twins are frequently employed. However, while these models are
typically accurate, they are custom-designed for individual systems, making
system-to-system (S2S) transferability difficult. This occurs even when
substantial similarities exist in the process dynamics across different
chemical systems. To address this challenge, we developed a novel
time-series-transformer (TST) framework that exploits the powerful transfer
learning capabilities inherent in transformer algorithms. This was demonstrated
using readily available process data obtained from different crystallizers
operating under various operational scenarios. Using this extensive dataset, we
trained a TST model (CrystalGPT) to exhibit remarkable S2S transferability not
only across all pre-established systems, but also to an unencountered system.
CrystalGPT achieved a cumulative error across all systems, which is eight times
superior to that of existing ML models. Additionally, we coupled CrystalGPT
with a predictive controller to reduce the variance in setpoint tracking to
just 1%.