A novel single-bed, "Snap-on" and standalone, medical oxygen concentrator design based on a rapid pressure swing adsorption process was investigated for continuous oxygen supply. The Snap-on concentrator design is easy to hook up to an existing compressed air source, and the unit can then be readily used to produce oxygen for medical applications. It is easily transportable and compared to a traditional oxygen concentrator with its dedicated compressor, the Snap-on concentrator is particularly relevant for the oxygen therapy needs of a larger number of patients in situations such as COVID-19. A commercially available LiLSX zeolite was used for the separation of oxygen from compressed ambient air. The experiments were performed at different feed air pressures using a constant supply of house air in the lab. Further, the device performance was also analyzed using a standalone medium size air compressor. The minimum bed size factor obtained with compressed house air was 100 lb/tons per day contained (TPDc) O 2 at a cycle time of 7 s, whereas the minimum bed size factor obtained with a medium size air compressor weighing about 12 lbs was 210 lb/TPD c O 2 at a cycle time of 14.5 s under the same feed pressures of 3.1 bar at an oxygen product purity of 90%. The product oxygen flow rate was nearly double for the same amount of adsorbent when using house air for the Snap-on design. The primary reason for this significantly higher oxygen production was the substantially higher and stable air throughput capacity of a typical house air compressor that enabled rapid cycling of the process at near-constant feed pressure compared to a medium size compressor used in a medical oxygen concentrator. The oxygen recovery was approximately 34% for both cases. Thus, the Snap-on oxygen concentrator was found to be easier to build and it delivered more oxygen for medical use compared to standalone units in locations where a constant supply of compressed feed air is available. This is typically the case in facilities such as hospitals, military medical camps and cruise ships. Further, the Snap-on design offers other benefits such as ease of transportation, higher reliability and lower weight.

A multivariable model predictive control (MPC) algorithm is developed for the control and operation of a rapid pressure swing adsorption (RPSA)‐based medical oxygen concentrator. The novelty of the approach is the use of all four step durations in the RPSA cycle as independent manipulated variables in a truly multivariable context. The RPSA has a complex, cyclic, nonlinear multivariable operation that requires feedback control, and MPC provides a suitable framework for controlling such a multivariable system. The multivariable MPC presented here uses a quadratic optimization program with integral action and a linear model identified using subspace system identification techniques. The controller was designed and tested in simulation using a complex, highly coupled, nonlinear RPSA process model. The model was developed with the least restrictive assumptions compared to those reported in the literature, thereby providing a more realistic representation of the underlying physical phenomena. The resulting MPC effectively tracks set points, rejects realistic process disturbances and is shown to outperform conventional PID control. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1234–1245, 2018

Rapid pressure swing adsorption (RPSA) is a gas separation technology used in the small-scale oxygen concentrator devices. These devices are commonly used to produce high purity (~90%) oxygen from air for oxygen rehabilitation therapy, but can also produce a much wider range of oxygen purities for other applications. RPSA is a complex, cyclic, nonlinear switched logic process resulting from the coupling of gas adsorption, heat transfer, flow reversal effects, and process logic switches. For RPSA devices to operate satisfactorily, feedback control is critical but challenging due to their inherent complexity. In this article, we present a piecewise linear model predictive control framework for operation and control of a single-bed RPSA system. A set of coupled, nonlinear partial differential equations with flow switching conditions is used as a plant model for the RPSA process. Subspace system identification with pseudo-random binary sequence signals applied to this plant model at multiple operating points is used to generate a family of piecewise linear models for use in the model predictive controller algorithm. Detailed descriptions of the RPSA plant model, the multiple linear model identification procedure, the controller formulation and model switching logic are presented. The closed-loop system is evaluated in simulation using several realistic set point tracking and disturbance rejection cases.