  In this work, we introduce a novel application of foundation models in the
domain of nonlinear chemical process modeling. Given the challenges of
obtaining accurate first-principles models for real-world chemical processes
and the inefficiency of rebuilding and retraining models for new chemical
processes, we pose a pivotal question: What if we could develop a single,
universal neural network (i.e., foundation model) capable of rapidly adapting
to modeling any new chemical process? To address this question, we propose a
meta-learning-based approach using Reptile to construct the foundation model,
followed by physics-informed adaptation to fine-tune it to new modeling tasks
using only a few data samples. To assess the effectiveness of our methodology,
we construct a foundation model for various chemical reactions in three
classical generic reactors, including continuous stirred tank reactors (CSTRs),
batch reactors (BRs), and plug flow reactors (PFRs). Our approach outperforms
conventional methods such as data-driven learning, physics-informed learning,
transfer learning, and pure meta-learning in a few-shot setting. Furthermore,
our method achieves rapid adaptation to new CSTRs, BRs, and PFRs using only a
few data samples from the designated tasks. Source code is available at
https://github.com/killingbear999/chemical-process-foundation-model.
