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For prediction and real-time control tasks, machine-learning (ML)-baseddigital twins are frequently employed. However, while these models aretypically accurate, they are custom-designed for individual systems, makingsystem-to-system (S2S) transferability difficult. This occurs even whensubstantial similarities exist in the process dynamics across differentchemical systems. To address this challenge, we developed a noveltime-series-transformer (TST) framework that exploits the powerful transferlearning capabilities inherent in transformer algorithms. This was demonstratedusing readily available process data obtained from different crystallizersoperating under various operational scenarios. Using this extensive dataset, wetrained a TST model (CrystalGPT) to exhibit remarkable S2S transferability notonly across all pre-established systems, but also to an unencountered system.CrystalGPT achieved a cumulative error across all systems, which is eight timessuperior to that of existing ML models. Additionally, we coupled CrystalGPTwith a predictive controller to reduce the variance in setpoint tracking tojust 1%.

CrystalGPT: Enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers