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<p>This paper introduces the design and development of an intelligent, ultraportable oxygen concentrator weighing around 5.5&#xa0;kg with closed-loop automation for optimal oxygen delivery. The system is Pressure Swing Adsorption (PSA) technology-based with a MAX30100 sensor for continuous monitoring of peripheral oxygen saturation (SpO₂) to modulate oxygen flow in real-time based on patient needs. Oxygen flow rate is managed by a DC motor-driven mechanism, combined with a stepper motor, and backed by force-torque analysis. A Proportional-Integral-Derivative (PID) control algorithm is used to control compressor speed, dynamically adjusting to sensor inputs to maintain desired oxygen purity levels. System validation using a PO 80 Pulse Oximeter ensured accurate tracking of SpO₂ and heart rate. Experimental results indicate improved energy efficiency, low response time, and customized oxygen therapy and demonstrate the appropriateness of the system for clinical and portable applications. The compressor and flow regulation valve, driven by a DC motor and stepper motor, respectively, respond to the controller outputs, with torque and pressure feedback ensuring stable flow rate and enhancing system reliability. PID controller gains were optimized using the Ziegler-Nichols method and iterative refinement to achieve optimum settling time and overshoot, while ensuring overall system stability and patient safety. Statistical analysis was used to evaluate system performance and reliability. A one-way analysis of variance (ANOVA) was used to determine significant differences between the significant parameters SpO₂ levels, response time, flow rate, and energy consumption among three oxygen delivery modes: intelligent control, fixed flow, and manual titration. Follow-up pair-wise t-tests established specific group differences, of which confidence intervals provided estimates of measurement precision. The statistical technique offers strong evidence for the better performance of the intelligent control system.</p>

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Intelligent oxygen delivery: a portable concentrator combining closed-loop automation, PSA, and Q-learning for optimized performance

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月24日

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  • Vijai Sivalingam,
  • Jayakumar Jayaraj,
  • Subha Hency Jose Paul,
  • Geetika Yadav

摘要

This paper introduces the design and development of an intelligent, ultraportable oxygen concentrator weighing around 5.5 kg with closed-loop automation for optimal oxygen delivery. The system is Pressure Swing Adsorption (PSA) technology-based with a MAX30100 sensor for continuous monitoring of peripheral oxygen saturation (SpO₂) to modulate oxygen flow in real-time based on patient needs. Oxygen flow rate is managed by a DC motor-driven mechanism, combined with a stepper motor, and backed by force-torque analysis. A Proportional-Integral-Derivative (PID) control algorithm is used to control compressor speed, dynamically adjusting to sensor inputs to maintain desired oxygen purity levels. System validation using a PO 80 Pulse Oximeter ensured accurate tracking of SpO₂ and heart rate. Experimental results indicate improved energy efficiency, low response time, and customized oxygen therapy and demonstrate the appropriateness of the system for clinical and portable applications. The compressor and flow regulation valve, driven by a DC motor and stepper motor, respectively, respond to the controller outputs, with torque and pressure feedback ensuring stable flow rate and enhancing system reliability. PID controller gains were optimized using the Ziegler-Nichols method and iterative refinement to achieve optimum settling time and overshoot, while ensuring overall system stability and patient safety. Statistical analysis was used to evaluate system performance and reliability. A one-way analysis of variance (ANOVA) was used to determine significant differences between the significant parameters SpO₂ levels, response time, flow rate, and energy consumption among three oxygen delivery modes: intelligent control, fixed flow, and manual titration. Follow-up pair-wise t-tests established specific group differences, of which confidence intervals provided estimates of measurement precision. The statistical technique offers strong evidence for the better performance of the intelligent control system.