Multi-objective ventilation optimization for indoor air quality, thermal comfort, and energy conservation in the post-pandemic era: A case study for a moving elevator.

The article focuses on optimizing elevator ventilation to mitigate the transmission of respiratory diseases in enclosed elevator cabins using a multi-objective genetic algorithm (MOGA) based on a response surface model (RSM). The study aims to reduce carbon dioxide (CO₂) concentration as a proxy for infectious aerosol presence, maintain thermal comfort, and conserve energy by adjusting air supply velocity, angle, and temperature during elevator movement with varying passenger loads (4 and 13 passengers). Computational fluid dynamics (CFD) simulations combined with Latin hypercube sampling (LHS) and the Kriging model were used to construct accurate response surfaces, which informed the MOGA to identify Pareto optimal ventilation parameters. The optimized ventilation schemes consistently set air supply temperature at 293 K, with specific air velocities and angles tailored to elevator direction and passenger density, achieving CO₂ concentrations below 100 ppm and satisfying health, comfort, and energy efficiency objectives.