MULTI-OBJECTIVE OPTIMIZATION OF TAF VENTILATION SYSTEMS WITH DIFFERENT INLET GEOMETRIES FOR PARTICLE CONTROL AND THERMAL COMFORT IN OPERATING ROOMS - Ghiffar Yanuar; Sholahudin; Kurniawan Teguh Waskito; Ridho Irwansyah (Multidiciplinary Output Research For Actual and International Issue (MORFAI), 2026)
Operating room ventilation plays an important role in controlling bacteria-carrying particles (BCP), reducing infection risk, and maintaining thermal comfort for medical staff. Temperature-controlled airflow (TAF) is a promising ventilation strategy because it supplies air through central and peripheral inlets with different temperatures to form a stable downward airflow. This study aims to optimize a TAF ventilation system by considering BCP concentration, energy consumption, and predicted mean vote (PMV). A three-dimensional operating room model was developed using computational fluid dynamics (CFD). Four operating parameters were varied using central composite design (CCD), while the peripheral inlet position was evaluated separately as a two-level geometry parameter. Airflow was solved using the RNG k–ε turbulence model, while BCP motion was modeled using the discrete phase model (DPM). CFD simulation results were used to train an artificial neural network (ANN) model, which was then integrated with a multi-objective genetic algorithm (MOGA). TOPSIS was used to select the best compromise solution from the Pareto front. The optimum condition was obtained at a central inlet velocity of 0.2735 m/s, peripheral inlet velocity of 0.1671 m/s, central inlet temperature of 21.8649 °C, peripheral inlet temperature of 22.0127 °C, and peripheral-to-central inlet distance of 2.0474 m. This configuration produced a BCP concentration of 0.9115 CFU/m³, energy consumption of 9.8832 kW, and PMV of −0.3186. These results indicate that the modified TAF configuration can produce low particle concentration, relatively low energy consumption, and acceptable thermal comfort in the operating room.