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.

翻译：本文提出了一种基础模型在化学反应器建模中的创新应用。基于第一性原理对实际化学反应器进行精确建模通常具有挑战性，且为每个新化学过程重建和重新训练模型效率低下。这引发了一个关键问题：我们能否开发一个单一的通用神经网络（即基础模型），使其能够快速适应反应器中的任何新化学过程？为此，我们提出了一种用于化学反应器建模的基础模型，该模型采用元学习方法，并随后基于少量数据样本对新任务进行物理信息微调。我们的模型旨在泛化至三种经典反应器类型：连续搅拌釜式反应器、间歇式反应器和活塞流反应器。与数据驱动学习、物理信息学习、迁移学习和元学习等传统方法相比，我们的方法在少样本场景下表现出更优的性能。具体而言，该模型能够快速适应不同反应器配置中具有变化整数阶次的未见反应，且仅需极少数据进行微调。源代码可在 https://github.com/killingbear999/chemical-reactor-foundation-model 获取。