Physics-based neural networks for simulation and synthesis of cyclic adsorption processes

A computationally faster and reliable modelling approach called a physics-based artificial neural network framework for adsorption and chromatography emulation (PANACHE) is developed. PANACHE uses deep neural networks for cycle synthesis and simulation of cyclic adsorption processes. The proposed approach focuses on learning the underlying governing partial differential equations in the form of a physics-constrained loss function to simulate adsorption processes accurately. The methodology developed herein does not require any system-specific inputs such as isotherm parameters. Accordingly, unique neural network models were built to fully predict the column dynamics of different constituent steps based on unique boundary conditions that are typically encountered in adsorption processes. The trained neural network model for each constituent step aims to predict the entire spatiotemporal solutions of different state variables by obeying the underlying physical laws. The proposed approach is tested by constructing and simulating four different vacuum swing adsorption cycles for post-combustion CO2 capture without retraining the neural network models. For each cycle, 50 simulations, each corresponding to a unique set of operating conditions, are carried out until the cyclic-steady state. The results demonstrated that the purity and recovery calculated from the neural network-based simulations are within 2.5% of the detailed model's predictions. PANACHE reduced computational times by 100 times while maintaining similar accuracy of the detailed model simulations.