Modeling and Simulation of Nitrogen Generation by Pressure Swing Adsorption for Power-to-Ammonia
2026-04-10 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and Science
AI summaryⓘ
The authors developed a detailed computer model to simulate how a pressure swing adsorption (PSA) system can separate nitrogen from air, which is important for making ammonia without using fossil fuels. Their model uses fundamental physics and chemistry to describe the complex processes happening inside PSA machines, like gas flow, heat changes, and how gases stick to materials called carbon molecular sieves. They built the simulation using efficient math methods and programming tools to handle the complicated calculations accurately. Their work helps provide an open and flexible tool to better understand and improve PSA systems for cleaner ammonia production.
Power-to-ammoniaPressure Swing Adsorption (PSA)Nitrogen generationCarbon molecular sievesThermodynamicsPartial differential-algebraic equationsFinite volume methodRunge-Kutta methodAutomatic differentiationCyclic steady state
Authors
Marcus J. Schytt, Lorenz T. Biegler, John B. Jørgensen
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.