Joseph Sang‐Il Kwon

Niranjan Sitapure, Joseph S Kwon

Most existing digital twins rely on data-driven black-box models, predominantly using deep neural recurrent, and convolutional neural networks (DNNs, RNNs, and CNNs) to capture the dynamics of chemical systems. However, these models have not seen the light of day, given the hesitance of directly deploying a black-box tool in practice due to safety and operational issues. To tackle this conundrum, hybrid models combining first-principles physics-based dynamics with machine learning (ML) models have increased in popularity as they are considered a 'best of both worlds' approach. That said, existing simple DNN models are not adept at long-term time-series predictions and utilizing contextual information on the trajectory of the process dynamics. Recently, attention-based time-series transformers (TSTs) that leverage multi-headed attention mechanism and positional encoding to capture long-term and short-term changes in process states have shown high predictive performance. Thus, a first-of-a-kind, TST-based hybrid framework has been developed for batch crystallization, demonstrating improved accuracy and interpretability compared to traditional black-box models. Specifically, two different configurations (i.e., series and parallel) of TST-based hybrid models are constructed and compared, which show a normalized-mean-square-error (NMSE) in the range of $[10, 50]\times10^{-4}$ and an $R^2$ value over 0.99. Given the growing adoption of digital twins, next-generation attention-based hybrid models are expected to play a crucial role in shaping the future of chemical manufacturing.

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%.

Mengmi Zhang, Claire Tseng, Karla Montejo et al.

Context reasoning is critical in a wide variety of applications where current inputs need to be interpreted in the light of previous experience and knowledge. Both spatial and temporal contextual information play a critical role in the domain of visual recognition. Here we investigate spatial constraints (what image features provide contextual information and where they are located), and temporal constraints (when different contextual cues matter) for visual recognition. The task is to reason about the scene context and infer what a target object hidden behind a flap is in a natural image. To tackle this problem, we first describe an online human psychophysics experiment recording active sampling via mouse clicks in lift-the-flap games and identify clicking patterns and features which are diagnostic for high contextual reasoning accuracy. As a proof of the usefulness of these clicking patterns and visual features, we extend a state-of-the-art recurrent model capable of attending to salient context regions, dynamically integrating useful information, making inferences, and predicting class label for the target object over multiple clicks. The proposed model achieves human-level contextual reasoning accuracy, shares human-like sampling behavior and learns interpretable features for contextual reasoning.