马鞍山特长高速公路隧道通风控制系统研究

Research on Ventilation Control System of Ma'an Shan Extra-long Highway Tunnel

运用前馈式神经网络理论、空气动力学理论以及模糊控制理论,针对马鞍山特长隧道的特点,以污染物浓度预测增量、污染物实测浓度和控制目标为输入,以需要增加或减少的风机组数为输出,构建了前馈式智能模糊控制系统,达到了隧道按需通风、节能减排的目的,解决了传统后馈式通风控制方式滞后、粗放的问题,利用Matlab软件对该系统进行仿真、调试,验证了该系统的有效性,通过计算马鞍山隧道一天开启风机的耗电量可知,采用前馈式智能模糊控制系统可以比采用传统后馈式通风控制系统相对节能12.21%左右,节能减排效果显著,对类似隧道工程的研究具有参考和指导意义。

In order to make the special long highway tunnels to be on-demand ventilation,to achieve the purpose of energy-saving emission reduction,the feed forward intelligent fuzzy control system is constructed. It's based on the characteristics of the Ma'an Shan highway tunnel project,the pollutant concentration predicted by incremental,the measured concentration of pollutants and control objectives for the input,the need to increase or decrease the number of fans for the output. Feed forward fuzzy control system consists of six modules: traffic flow prediction module,fuzzy controller,execution module,detection module,contaminant dynamic module and control object. The feed-forward neural network theory,aerodynamics theory and fuzzy control theory were used in this control system to solve the problem of lag and extensive of traditional back-feed ventilation control system. The system is simulated and debugged by Matlab,which proves the effectiveness of the system. The feed forward intelligent fuzzy control system can save energy by 12. 21% compared with the traditional back-feed ventilation control system,by calculating the power consumption of the fan in Ma'an Shan Tunnel one day. Energy saving and emission reduction effect is remarkable. It has reference and guidance significance to the similar tunnel engineering research.