Design and Optimization Framework for High-Efficiency Air Filtration Systems in Portable Respiratory Ventilators

Abstract

Portable respiratory ventilators are essential for helping individuals with reduced lung function. Ensuring clean, contaminant-free air is critical for patient safety, but current portable ventilators have hurdles in obtaining high filtration efficiency while maintaining airflow and device portability. This research develops an optimized framework for high-efficiency air filtration systems in portable respiratory ventilators by modeling key operating parameters, including particle removal efficiency, airflow resistance, and energy consumption. The model integrates suitable filter materials HEPA, electrostatic and activated carbon for medical-grade particle removal. Computational Fluid Dynamics (CFD) simulations are employed to optimize filter geometry and pleating configuration for balanced airflow and effective particle capture. A Red Deer War Optimization (RDWO) algorithm, a hybrid metaheuristic combining War Strategy Optimization (WSO) and Red Deer Optimization (RDO), is applied to determine the best combination of filter type, thickness, and pleat arrangement to boost system performance. The optimum filter design is incorporated into a portable ventilation system and experimentally validated under simulated respiratory conditions to evaluate filtration efficiency, pressure drop, and energy consumption. The optimized filtration system achieved higher particle removal efficiency, reduced airflow resistance, and extended battery life compared to conventional designs. The suggested design and optimization framework offers a practical and effective method for building high-performance air filtering systems in portable respiratory ventilators, therefore improving patient safety, device portability, and energy efficiency.