Implementation of an embedded model predictive controller for a novel medical oxygen concentrator

• Published in: Computers & chemical engineering Vol. 160; p. 107706
• Main Authors: Urich, Matthew D., Vemula, Rama Rao, Kothare, Mayuresh V.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2022
• Subjects: Cyclic systems; Embedded control; Medical oxygen concentrator; Model Predictive Control; Sub-space identification; Sub-space identification; Cyclic systems; Medical oxygen concentrator; Embedded control; Model Predictive Control
• ISSN: 0098-1354
• DOI: 10.1016/j.compchemeng.2022.107706

•Medical Oxygen Concentrators are growing in importance for COVID-19 and COPD.•MOCs operate complex cyclic RPSA processes that are difficult to control.•Advanced model-based predictive control provides a framework for exploiting the nonlinear interactions and constraints.•Integration of control with the MOC device requires embedding the control algorithm in hardware. Medical Oxygen Concentrators (MOCs) produce high purity oxygen from ambient air for medical therapies, and can range in size from large stationary units to small ultra-portable devices. These devices use a complex Rapid Pressure Swing Adsorption (RPSA) cyclic process which is subject to many process disturbances. Feedback control is required to operate a MOC reliably in different operating conditions and in presence of disturbances. Recently, a multivariable Model Predictive Controller (MPC) for a single-bed MOC device was developed in simulation, and we now present an implementation of this MPC for a lab-scale MOC device. The single-bed MOC uses a four-step RPSA cycle which can be controlled in real-time by adjusting the four cycle step durations to control the product oxygen concentration and product storage tank pressure. The MPC uses a linear model, identified using experimental sub-space system identification techniques, and an embedded convex quadratic optimization problem for making control decisions. This work presents the implementation of this MPC algorithm using Raspberry Pi hardware for the single-bed MOC device. The complete closed-loop system is evaluated using various set point tracking and disturbance case studies.