用于从实验数据学习和优化化学传输过程的可微混合建模

中文摘要 AI 辅助

可靠的传输模型对于建模和优化许多化学工程过程至关重要，但大多数模型采用人工挑选的本构定律，可能无法反映实际情况，且通常假设初始条件完全已知。这两个限制会显著使模型预测产生偏差，并在预测和控制场景中导致系统性误差。黑箱神经替代模型可更好地匹配实际示例数据，但仅限于其训练任务，且无法验证物理一致性。本文针对传输过程（具体为群体平衡方程情况）提出了一种通用可微混合建模框架，该框架将JAX有限体积群体平衡求解器与可学习的神经网络组件相结合，这些组件经过训练可从真实实验数据中同时发现本构定律并拟合初始条件，从而能更好地建模真实实验传输系统。此外，本文利用该框架的可微性进行过程优化，直接针对目标量优化实验设置。本研究凸显了此类可微混合建模框架在学习和优化涉及质量、能量和/或动量传输的任何化学分离过程方面的巨大潜力。

英文摘要

Reliable transport models are essential when modelling and optimising many chemical engineering processes, yet, most models assume hand-picked constitutive laws which may not reflect reality, and often assume initial conditions are known exactly. Both restrictions can significantly bias model predictions and lead to systematic error when used in predictive and control settings. Black-box neural surrogate alternatives for modelling can better match real example data, but are confined to the task they were trained on and cannot be interrogated for physical consistency. Here we introduce a general-purpose differentiable hybrid modelling framework for transport processes, specifically for the case of population balance equations. Our framework integrates a JAX finite volume population balance solver with learnable neural network components which are trained to both discover constitutive laws and fit initial conditions from real experimental data, allowing us to better model real experimental transport systems. Furthermore, we use our framework for process optimisation, using its differentiability to allow us to direct optimising experimental settings for quantities of interest. This work highlights the huge potential of such differentiable hybrid modelling frameworks for learning and optimising any given chemical separation which involves mass, energy, and/or momentum transport.

发表机构

• The University of Manchester（曼彻斯特大学）
• Imperial College London（帝国理工学院）

机构由 AI 辅助整理，请以论文原文为准。