Adaptive gas supply system for membrane oxygenator using online model identification and control in normothermic machine perfusion.

OBJECTIVE: This work addresses a critical challenge in normothermic machine perfusion (NMP) : the precise and safe control of the oxygenator's gas supply. The objective is to develop a novel control framework that integrates real-time model identification with adaptive pressure control, aiming to dynamically regulate the partial pressure of oxygen in the blood while preventing critical failure modes like plasma leakage.

METHODS: By analyzing the oxygenation process within the artificial lung membrane, we demonstrate that the gas supply system's input-output behavior can be modeled using a discrete-time autoregressive model with exogenous input (ARX). In this model, the concentrations of both gas and liquid phases are related to the gradients of transmembrane pressure. An online parameter identification employs a forgetting factor recursive least squares (FFRLS) algorithm to control the transmembrane pressure difference. The algorithm enables adaptive tuning of a proportional-integral-derivative (PID) controller, and controller parameters are dynamically updated using real-time model estimates. This adaptive mechanism ensures precise sweep gas pressure regulation. Animal experiments utilizing a prototype extracorporeal membrane oxygenation (ECMO) platform validated the integration of online transmembrane pressure identification and adaptive control.

RESULT: It achieved rapid setpoint tracking with a settling time of less than 4 s and maintained stable transmembrane pressure with a tracking error of less than ±1 mmHg, even during significant blood pressure fluctuations. Blood gas analysis confirmed the system's efficacy, successfully modulating PaO2 to a target normoxic range (90-200 mmHg) while simultaneously preventing plasma leakage, which was observed at excessive pressure differentials.

CONCLUSION: This study proposed a novel adaptive control framework for NMP oxygenators,demonstrating a strategy that simultaneously ensures oxygenator safety by preventing plasma leakage and enables therapeutic regulation of PaO2 through on-line model identification, with its clinical potential confirmed in preclinical animal trials. This approach provides a robust foundation for improving organ viability during perfusion and prolonging the functional lifespan of the oxygenator, establishing a new pathway toward safer and more effective organ preservation.