Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation
Zihao Wang, Zhe Wu
TL;DR
The paper addresses the challenge of generalizing reactor models across diverse chemical systems by proposing a foundation-model-inspired framework that combines Reptile-based meta-learning with physics-informed adaptation. The method trains a unified RNN on a broad set of simulated CSTRs, BRs, and PFRs, then adapts to unseen reactions using few-shot data and collocation-based physics losses, with approximate kinetic parameters incorporated into the loss. Results show that Reptile with physics-informed adaptation dramatically improves few-shot performance across all three reactor types compared to data-driven, transfer learning, and standard PINN baselines, and the approach scales to varying integer reaction orders via ensemble methods. While not claiming universal applicability, the work provides a significant step toward domain-specific foundation models in chemical engineering, with potential impact on process monitoring, optimization, and control, and outlines clear avenues for broader validation and extension.
Abstract
Developing accurate models for chemical reactors is often challenging due to the complexity of reaction kinetics and process dynamics. Traditional approaches require retraining models for each new system, limiting generalizability and efficiency. In this work, we take a step toward foundation models for chemical reactor modeling by introducing a neural network framework that generalizes across diverse reactor types and rapidly adapts to new chemical processes. Our approach leverages meta-learning to pretrain the model on a broad set of reactor dynamics, enabling efficient adaptation to unseen reactions with minimal data. To further enhance generalizability, we incorporate physics-informed fine-tuning, ensuring physically consistent adaptation to new reactor conditions. Our framework is evaluated across three integer-order fundamental reactor types - continuous stirred tank reactors, batch reactors, and plug flow reactors - demonstrating superior few-shot adaptation compared to conventional data-driven, physics-informed, and transfer learning approaches. By combining meta-learning with physics-informed adaptation, this work lays the foundation for a generalizable modeling framework, advancing the development of foundation models for chemical engineering applications. Source code is available at https://github.com/killingbear999/chemical-reactor-foundation-model.