A Physics-Guided Machine Learning Framework Enabling Interpretable and Transferable Cascade Reactor Modeling:...

Cascade reactors are vital for separation and purification processes such as metal recovery and wastewater treatment, owing to their enhanced reaction efficiency compared to single-stage systems. Yet, their simulation and optimization remain challenging. First-principles models are prohibitively complex, and purely data-driven approaches suffer from data scarcity and inadequate transferability across reactor configurations. To address these challenges, we propose a physics-guided reactor network (PG-RN), a hybrid machine-learning framework that structurally embeds residence time distribution (RTD) theory into machine learning. PG-RN achieved predictive accuracy comparable to widely used baselines in data-rich scenarios and outperformed them under data scarcity, while providing interpretable RTD profiles consistent with theoretical expectations. Building on this framework, a differentiated transfer learning strategy guided by physical knowledge was introduced to enable efficient transfer across reactor configurations. It was successfully validated on both simulated nonideal reactors and a real-world metal recovery process for spent lithium-ion batteries, demonstrating improved predictive accuracy and reduced data requirements for characterizing complex cascade reactor systems. Notably, it achieved a 22% performance improvement on experimental data compared to the nontransfer baseline. Overall, this work establishes a data-efficient, interpretable, and transferable modeling framework for complex cascade reactor systems, offering a promising pathway to the development of sustainable metal recovery and related separation processes.