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.

翻译：本文提出了一种将基础模型应用于非线性化工过程建模的新方法。针对实际化工过程中难以获取精确第一性原理模型，以及为新化工过程重建和重新训练模型效率低下的问题，我们提出一个关键问题：能否开发一个单一的通用神经网络（即基础模型），使其能够快速适应任何新化工过程的建模？为解决这一问题，我们提出了一种基于元学习的方法，利用Reptile算法构建基础模型，然后通过物理信息自适应方式，仅使用少量数据样本即可将其微调至新的建模任务。为评估该方法的有效性，我们针对三种经典通用反应器（连续搅拌釜式反应器CSTRs、间歇式反应器BRs和管式反应器PFRs）中的各类化学反应构建了基础模型。在少样本场景下，我们的方法优于数据驱动学习、物理信息学习、迁移学习和纯元学习等传统方法。此外，该方法仅需从指定任务中获取少量数据样本，即可快速适应新的CSTRs、BRs和PFRs。源代码获取地址：https://github.com/killingbear999/chemical-process-foundation-model。