Deep Operator Networks for Surrogate Modeling of Cyclic Adsorption Processes with Varying Initial Conditions

TL;DR

This paper demonstrates that Deep Operator Networks can effectively serve as surrogates for cyclic adsorption process simulations, accurately predicting solutions across diverse initial conditions and functional forms, thus accelerating process optimization.

Contribution

The study applies DeepONets to model PDE-based cyclic adsorption processes, showcasing their ability to generalize across varying initial conditions and improve simulation efficiency.

Findings

DeepONets accurately predict solutions within training ranges.

Models generalize well to unseen initial conditions.

Surrogates significantly reduce computational costs.

Abstract

Deep Operator Networks are emerging as fundamental tools among various neural network types to learn mappings between function spaces, and have recently gained attention due to their ability to approximate nonlinear operators. In particular, DeepONets offer a natural formulation for PDE solving, since the solution of a partial differential equation can be interpreted as an operator mapping an initial condition to its corresponding solution field. In this work, we applied DeepONets in the context of process modeling for adsorption technologies, to assess their feasibility as surrogates for cyclic adsorption process simulation and optimization. The goal is to accelerate convergence of cyclic processes such as Temperature-Vacuum Swing Adsorption (TVSA), which require repeated solution of transient PDEs, which are computationally expensive. Since each step of a cyclic adsorption process starts from the final state of the preceding step, effective surrogate modeling requires generalization across a wide range of initial conditions. The governing equations exhibit steep traveling fronts, providing a demanding benchmark for operator learning. To evaluate functional generalization under these conditions, we construct a mixed training dataset composed of heterogeneous initial conditions and train DeepONets to approximate the corresponding solution operators. The trained models are then tested on initial conditions outside the parameter ranges used during training, as well as on completely unseen functional forms. The results demonstrate accurate predictions both within and beyond the training distribution, highlighting DeepONets as potential efficient surrogates for accelerating cyclic adsorption simulations and optimization workflows.