Physics-Informed Neural Networks as Fast Surrogate Models for Electrochemical Flow Reactors

Abstract

This work presents a physics-informed neural network (PINN) for modeling a transient two-dimensional electrochemical flow reactor with diffusion, migration, convection, and nonlinear anodic Butler--Volmer kinetics. The model is trained without labeled concentration data by embedding the governing transport equation and all initial and boundary conditions into a composite loss function. Spatial and temporal coordinates together with anodic overpotential, temperature, inlet concentration, maximum flow velocity, and diffusivity are used as inputs, allowing the network to predict concentration fields over a broad operating domain. Validation against finite-difference-based solutions shows strong agreement for representative transient and near-steady cases, with a mean relative space--time error of $(9.99 \pm 0.65)\times10^{-3}$ (sub-percent level) across the conditioning domain. PINN inference is faster than a traditional finite difference solver by a factor of $5.34$, thus reducing runtime by $81.3\%$. Generalization tests further show that the surrogate remains robust under in-domain boundary-focused sampling and controlled extrapolation, although anodic overpotential is the most challenging parameter due to its exponential effect on interfacial kinetics. The results indicate that physics-informed neural networks can serve as accurate and efficient parametric surrogates for electrochemical transport problems and provide a foundation for low-computational-cost digital-twin modeling of electrochemical flow reactors.