A New Model of Air–Oxygen Blender for Mechanical Ventilators Using Dynamic Pressure Sensors

Author:

Soares Gabryel F.1,Fernandes Gilberto2ORCID,Almeida Otacílio M.1ORCID,Lima Gildario D.3,Rodrigues Joel J. P. C.4ORCID

Affiliation:

1. Department of Electrical Engineering, Federal University of Piauí (UFPI), Teresina 64049-550, Brazil

2. Computer Science Department, State University of Londrina (UEL), Londrina 86057-970, Brazil

3. Federal University of Delta do Parnaíba (UFDPar), Parnaíba 64202-020, Brazil

4. COPELABS, Lusófona University, 1749-024 Lisbon, Portugal

Abstract

Respiratory diseases are among the leading causes of death globally, with the COVID-19 pandemic serving as a prominent example. Issues such as infections affect a large population and, depending on the mode of transmission, can rapidly spread worldwide, impacting thousands of individuals. These diseases manifest in mild and severe forms, with severely affected patients requiring ventilatory support. The air–oxygen blender is a critical component of mechanical ventilators, responsible for mixing air and oxygen in precise proportions to ensure a constant supply. The most commonly used version of this equipment is the analog model, which faces several challenges. These include a lack of precision in adjustments and the inspiratory fraction of oxygen, as well as gas wastage from cylinders as pressure decreases. The research proposes a blender model utilizing only dynamic pressure sensors to calculate oxygen saturation, based on Bernoulli’s equation. The model underwent validation through simulation, revealing a linear relationship between pressures and oxygen saturation up to a mixture outlet pressure of 500 cmH2O. Beyond this value, the relationship begins to exhibit non-linearities. However, these non-linearities can be mitigated through a calibration algorithm that adjusts the mathematical model. This research represents a relevant advancement in the field, addressing the scarcity of work focused on this essential equipment crucial for saving lives.

Funder

Brazilian National Council for Scientific and Technological Development-CNPq

Publisher

MDPI AG

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