Research On Environmental Data Processing And Ventilation Control Model Of Highway Tunnel

Although highway tunnels have provided many favorable conditions，its operatingexpenses is still a problem which must be faced. In order to ensure the traffic safety in thetunnel, the thesis has established a tunnel ventilation control model based on fuzzy neuralnetwork. The model has realized the real-time control of the ventilation, achieving achievedthe purpose of security and energy-saving.The thesis is based on TongHuang Tunnel, and the data is from its monitoring systemdatabase. Firstly, the current primary ventilation methods and their control methods areintroduced, which help establishing a mathematical model for the ventilation system ofTongHuang Tunnel. Secondly, because of the complexity and particularity of the tunnelenvironmental data, a software to analysis and process the data was designed. It can easilyimplement the query and saving of large amounts of data, sort and interpolate, filter, unify thesampling time, etc. The complex relational relationship among data is analyzed qualitativelyand quantitatively, and the result provides the input data for the prediction model. Thirdly, thetraditional neural network algorithm has been improved, and a good tunnel data predictionmodel has been established, which can predict the concentration trends of CO and VI in shorttime, and the trends can be used as the input variables of ventilation control model. Finally,the fuzzy control and neural network control are effectively combined by comparativeanalyzing, the membership functions and fuzzy rules for tunnel ventilation control model aredetermined, and then the tunnel ventilation control model based on fuzzy neural network isestablished.Through the simulation analysis of models established in this thesis, the prediction errorof data prediction model can be controlled within an allowable range, so that the result can beapplied to the study of control algorithms. And the result of tunnel ventilation control modelsimulation shows that, through the reasonable start-stop control of fans, the costs of the tunnelventilation can be saved more than20%, energy saving effect is obvious.