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Exploring the potential of time-series transformers for process modeling and control in chemical systems: An inevitable paradigm shift? by Niranjan Sitapure; Joseph Sang-Il Kwon is a Engineering article available to read on EtoBox.

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The last two years have seen groundbreaking advances in natural language processing (NLP) with the advent of applications like ChatGPT, Codex, and ChatSonic. This revolution is powered by the development of cutting-edge transformer models that leverage multiheaded attention mechanisms, positional encoding, and highly efficient transfer learning capabilities. Despite these remarkable advances, there is still work to be done to fully realize the practical applicability of transformers in chemical systems. Thus, we are excited to present our latest work, which highlights the immense potential of transformers for non-trivial multivariate time-series prediction tasks with high-value implications in process monitoring, control, and optimization. Specifically, impressive prediction capabilities of first-generation time-series transformers (TSTs) were demonstrated by developing, testing, and comparing TSTs with existing models. Further, the practical applicability of TSTs was highlighted by developing a first-of-a-kind TST-based model predictive controller (MPC). More importantly, the current work provides a concrete foundation for exploring promising new directions, such as the developmen

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Author

Niranjan Sitapure; Joseph Sang-Il Kwon

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)