Multi-objective optimization of indoor air quality control and energy consumption minimization in a subway ventilation system

Abstract

Ventilation systems in subway stations have been mainly used to control indoor air pollutants. Conventional control systems using manual control or proportional integral derivative (PID) control are widely implemented without considering the energy cost. In this paper, a multi-objective optimization (MOO) which determines optimal set points of model predictive control (MPC) is developed to ensure healthy indoor air quality (IAQ) as well as to minimize energy consumption. First, three first-order plus time delay models are obtained by using the system identification method, which can be used to control the concentration of particulate matter (PM) at the platform. The input variable of the process model is the speed of the fan inverter and the other two disturbances are the train schedule and the concentration of outside PM10. Next, based on the understanding of IAQ dynamics, the MPC controller that provides optimal control actions is then developed in the D-station of the Korean subway. The multi-objective genetic algorithm with Pareto optimal objective is adopted to determine the optimal set points of the MPC. The results indicate that the performance of the proposed multi-objective optimization ventilation control is superior to that of manual control, both in terms of IAQ and energy savings.