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隧道施工通风中风机的自动控制算法主要面临两个问题:(1)传感器数值定量化分析不成熟;(2)传感器安装位置研究不足。文章提出了一种三段式隧道施工通风自动控制算法,分为全速通风阶段、自动调节阶段和低速运转阶段。其中全速通风阶段由震荡波传感器检测到的爆破动作触发,至污染物峰值经过传感器网络安装位置为止。此后,系统进入自动调节阶段。本文采用神经网络模型对现场风机的调节规律进行学习,并将控制信号作用于变频器。低速运转阶段按照相关规程实现隧道环境参数的持续稳定。本系统在天目山杭黄隧道施工现场进行了测试分析,取得了较好的实验效果。
Tunnel construction ventilation fan automatic control algorithm is mainly faced with two problems: (1) quantitative analysis of sensor values immature; (2) the lack of research on the sensor installation location. This paper presents a three-stage tunnel construction ventilation automatic control algorithm, divided into full speed ventilation stage, automatic adjustment stage and low speed operation stage. The full-speed ventilation phase is triggered by the blasting action detected by the Sasser sensor until the peak of the pollutant passes through the sensor network installation location. After that, the system enters the automatic adjustment phase. In this paper, neural network model on-site regulation of fan learning, and the role of control signals in the inverter. Low-speed operation stage in accordance with the relevant regulations to achieve continuous stability of the tunnel environment parameters. The system has been tested and analyzed in the construction site of the tunnel in Tianmu Mountain, Hangzhou and achieved good experimental results.