Modeling and Simulation of Nitrogen Generation by Pressure Swing Adsorption for Power-to-Ammonia

TL;DR

This paper develops a first-principles, dynamic model of a pressure swing adsorption system for nitrogen generation, enabling simulation and optimization of decentralized Power-to-Ammonia processes.

Contribution

It introduces a thermodynamically consistent, PDE-based PSA model implemented in Julia, capable of simulating cyclic steady states with high efficiency.

Findings

The model accurately predicts nitrogen purity and recovery.

Spatial and temporal discretization strategies impact simulation results.

Thermodynamic assumptions influence nitrogen separation performance.

Abstract

Power-to-ammonia (P2A) provides a carbon-free alternative to conventional ammonia production by replacing fossil-based feedstocks with electrolytic hydrogen and nitrogen from air separation. For decentralized P2A systems, pressure swing adsorption (PSA) offers a flexible alternative to cryogenic air separation. However, its industrial implementations are largely proprietary, and open, first-principles models capable of simulating its cyclic, nonlinear transport are scarce in literature. This work presents a first-principles, dynamic, one-dimensional model of a PSA superstructure for nitrogen generation, formulated with thermodynamically consistent equations of state, coupling multicomponent mass, energy, and momentum balances with kinetically limited adsorption on carbon molecular sieves. The resulting system of partial differential-algebraic equations (PDAEs) is semi-discretized using the finite volume method, integrated using diagonally implicit Runge-Kutta methods, and cyclic steady states (CSS) are computed via shooting-based solution methods. The framework is implemented in Julia, combining analytical derivatives with automatic differentiation and utilizing sparse linear algebra for efficient solution of the arising large nonlinear systems. The framework is demonstrated on a two-bed PSA cycle for air separation, comparing spatial and temporal discretization strategies, CSS solution methods, and the effects of ideal versus real-gas thermodynamics on predicted nitrogen purity and recovery. The proposed framework establishes an extensible basis for PSA simulation and optimization.