Multi-objective optimization of operation strategy in snow melting system for airfield runway using genetic algorithm: A case study in Beijing Daxing International Airport

Abstract

A hydronic snow melting pavement system is an efficient and sustainable snow removal approach. However, the conventional operation method remained unclear and usually led to a waste of energy. This study developed a thermal-economic model to assess this system's economic cost and snow melting effect. The multi-objective optimization (MOO) model and genetic algorithm (GA) were adopted to optimize the operation strategy, which includes fluid temperature (FT) and idling duration (ID). The accuracy of the MOO model was verified by a convergence study. The optimal results were organized in nomograms and implemented at Beijing Daxing International Airport. The results showed that the tournament selection method performs better in MOO solving. The optimal results indicated that it would obtain a better effect to balance the economic benefit and snow melting effect by adjusting FT than ID. And the MOO model can export the appropriate strategy in response to various snowfall conditions. Specifically, the optimal strategy implementation in the case study improved the snow-free duration ratio by 126% and saved the economic cost by 7% compared to the conventional strategies. The investigation provided an alternative operation strategy to minimize the energy cost for this system while ensuring the snow melting effect.