In the ventilation management of urban traffic tunnels,to improve the efficiency of ventilation system and environmental quality,an intelligent tunnel fan control method based on real-time monitoring was proposed in this paper.This method can predict pollutant concentration trends using an embedded semi-empirical physical model and carry out fuzzy control over tunnel fan operations,including opening,closing,and frequency adjustments.Additionally,by continuously refines the embedded model's parameters through machine learning,the control strategies can be optimized,providing more accurate data support and intelligent control strategies for fan regulation.Firstly,the content and working process of the intelligent tunnel fan control method were described in detail.Subsequently,combined with the measured PM25 data from representative highway tunnels in Dalian,the economic analysis of this control method was conducted using the Dalian Bay Immersed Tunnel Project as a case study.The results show that this method can reduce the ventilation system operational costs and has great energy-saving potential.
维普
万方数据