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

Abstract

Cases of respiratory disease transmission in enclosed elevators have been reported frequently. In the post-pandemic era, in order to mitigate the spread of respiratory diseases in moving elevators, a multi-objective genetic optimization method based on a response surface model is used to optimize the elevator ventilation. The ventilation parameters were optimized for three objectives: reducing carbon dioxide concentration, maintaining human thermal comfort, and achieving energy conservation. First, a response surface model is established using the computational fluid dynamics method and the Kriging model to correlate the design variables (air supply velocity in x, y, and z directions and air supply temperature) with the output function (CO2 concentration, average temperature, and average velocity). Subsequently, the Pareto optimal solution set of ventilation parameters was obtained by employing a multi-objective genetic algorithm. Finally, the optimal air supply velocity, angle, and temperature were obtained for both peak periods of elevator traffic (13 passengers) and other situations (4 passengers) when the elevator is moving up and down, which satisfy the objectives of health, comfort, and energy conservation.