Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation

• URL: http://arxiv.org/abs/2405.11752v2
• Date: Wed, 23 Oct 2024 08:29:20 GMT
• Title: Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation
• Authors: Zihao Wang, Zhe Wu,
• Abstract summary: We present a novel application of foundation models for chemical reactor modeling. Our model is designed to generalize across three classic reactor types. It shows rapid adaptation to unseen reactions with varying integer orders across different reactor set-ups.
• Score: 14.835081385422653
• License:
• Abstract: In this work, we present a novel application of foundation models for chemical reactor modeling. Accurate modeling of real-world chemical reactors through first-principles is often challenging, and the process of rebuilding and retraining models for each new chemical process is inefficient. This raises a critical question: can we develop a single, universal neural network (i.e., a foundation model) that can rapidly adapt to any new chemical process in a reactor? To address this, we propose a foundation model for chemical reactor modeling that employs a meta-learning approach, followed by physics-informed fine-tuning on new tasks with only a few data samples. Our model is designed to generalize across three classic reactor types: continuous stirred tank reactors, batch reactors, and plug flow reactors. Compared to conventional methods such as data-driven learning, physics-informed learning, transfer learning, and meta-learning, our approach demonstrates superior performance in few-shot scenarios. Specifically, it shows rapid adaptation to unseen reactions with varying integer orders across different reactor set-ups, requiring minimal data for fine-tuning. Source code is available at https://github.com/killingbear999/chemical-reactor-foundation-model.

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