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. 2021 Jul 12;11(1):14317.
doi: 10.1038/s41598-021-93796-3.

Flexible oxygen concentrators for medical applications

Affiliations

Flexible oxygen concentrators for medical applications

Akhil Arora et al. Sci Rep. .

Abstract

Medical oxygen concentrators (MOCs) are used for supplying medical grade oxygen to prevent hypoxemia-related complications related to COVID-19, chronic obstructive pulmonary disease (COPD), chronic bronchitis and pneumonia. MOCs often use a technology called pressure swing adsorption (PSA), which relies on nitrogen-selective adsorbents for producing oxygen from ambient air. MOCs are often designed for fixed product specifications, thereby limiting their use in meeting varying product specifications caused by a change in patient's medical condition or activity. To address this limitation, we design and optimize flexible single-bed MOC systems that are capable of meeting varying product specification requirements. Specifically, we employ a simulation-based optimization framework for optimizing flexible PSA- and pressure vacuum swing adsorption (PVSA)-based MOC systems. Detailed optimization studies are performed to benchmark the performance limits of LiX, LiLSX and 5A zeolite adsorbents. The results indicate that LiLSX outperforms both LiX and 5A, and can produce 90% pure oxygen at 21.7 L/min. Moreover, the LiLSX-based flexible PVSA system can manufacture varying levels of oxygen purity and flow rate in the range 93-95.7% and 1-15 L/min, respectively. The flexible MOC technology paves way for transitioning to an envisioned cyber-physical system with real-time oxygen demand sensing and delivery for improved patient care.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Envisioned cyber-physical system for online oxygen demand sensing and home-based delivery using adsorption-based medical oxygen concentrator (MOC).
Figure 2
Figure 2
An illustrative depiction of product specifications Pareto frontier and feasible domain for pressure swing adsorption (PSA)-based flexible MOC operation. The key objective is to push the Pareto frontier and maximize the feasible PSA operation area using the simulation-based optimization framework.
Figure 3
Figure 3
Graphical depiction of 8 process operation modes that constitute a PSA-based MOC cycle. The blue circles denote the adsorbent particles whereas the darker orange shade represents the part of the adsorbent column saturated with oxygen adsorbate.
Figure 4
Figure 4
Experimental equilibrium adsorption capacity data and fitted isotherms for candidate adsorbents in adsorption-based MOC. (a) LiX, (b) LiLSX and (c) 5A zeolites with experimental data of Rege and Yang, Zhu et al. and Talu et al., respectively.
Figure 5
Figure 5
Process simulation data and output performance metrics for LiX zeolite. (a, b) PSA cycle and (c, d) PVSA cycle.
Figure 6
Figure 6
Process simulation data and output performance metrics for LiLSX zeolite. (a, b) PSA cycle and (c, d) PVSA cycle.
Figure 7
Figure 7
Process simulation data and output performance metrics for 5A zeolite. (a, b) PSA cycle and (c, d) PVSA cycle.
Figure 8
Figure 8
Purity-recovery Pareto frontier obtained using optimization-based analysis. (ad) PSA with minimum production (a) 1, (b) 5, (c) 10 and (d) 15 L/min. (eh) PVSA with minimum production (e) 1, (f) 5, (g) 10 and (h) 15 L/min.
Figure 9
Figure 9
Purity-BSF Pareto frontier obtained using optimization-based analysis. (ad) PSA with minimum production (a) 1, (b) 5, (c) 10 and (d) 15 L/min. (eh) PVSA with minimum production (e) 1, (f) 5, (g) 10 and (h) 15 L/min.
Figure 10
Figure 10
Flexible (a) PSA and (b) PVSA operation for generating product oxygen with different purity and production rate specifications.
Figure 11
Figure 11
Comparison between actual and target oxygen (a) flow rate and (b) purity for meeting time-varying oxygen flow rate demand with 90% minimum purity.

References

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