Multi-Objective Optimization of TAF Systems: Enhancing Air Cleanliness and Energy Efficiency in Surgical Environments

Abstract

Hospital operating room ventilation systems are essential to prevent the spread of bacteria-carrying particles (BCP) and reduce the risk of surgical site infection (SSI). In addition to ensuring air quality, managing the energy consumption is a critical concern, particularly for energy-intensive operating rooms. This study focused on optimizing temperature-controlled airflow (TAF) ventilation systems to achieve a balance between air cleanliness and energy efficiency. The key variables analyzed included the velocity of the central inlet, velocity of the peripheral inlet, and temperature of the central inlet. This study employs computational fluid dynamics (CFD) to simulate particle movement, with model validation based on established literature data and mathematical calculations to determine the energy consumption. A central composite design (CCD) provides the experimental design, whereas artificial neural networks (ANN) predict the outcomes for untested scenarios, supporting multi-objective optimization through three algorithms: MOGA, MODA, and MOGOA. This study identified the optimal settings for the TAF system to achieve a balance between BCP concentration and energy consumption. The recommended parameters are as follows: central inlet airflow should maintain a velocity between 0.27–0.28 m/s and a temperature range of 20.5°C to 21.5°C, whereas peripheral inlet airflow should have a velocity of 0.17–0.18 m/s with a consistent temperature of 23°C. These settings strike a balance between ensuring air cleanliness and minimizing energy consumption, thereby enhancing the overall hospital efficiency.