煤矿工作面需风量预测及通风机智能调速控制

Prediction of Air Demand at Coal Mine Working Face and Intelligent Speed Control of Ventilation Fan

为进一步提升工作面的生产安全性和生产效率,保证工作面需风量与供风量的实时匹配性控制具有重要意义。以FBCDNO.7.5通风机为研究对象,在对其基本结构参数和工况分析的基础上,基于GA算法对神经网络模型不断迭代实现对需风量的精准预测;以PID控制器和T-S神经网络控制模型为核心构建通风机智能调速控制系统,并实现对通风机运行状态的精准控制,最终达到了通风机供风量与需风量相互匹配的目的。

In order to further improve the safety and productivity of the working face,it is important to ensure the real-time matching control of the air demand and supply of the working face.Taking the local ventilation fan as an example,based on the analysis of its basic structural parameters and working conditions,the GA algorithm is used to iterate the neural network model to achieve accurate prediction of air demand;the PID controller and the T-S neural network control model are used as the core to build the intelligent speed control system for the ventilation fan,and to achieve accurate control of the operation status of the ventilation fan,finally achieving the purpose of matching the air supply and air demand of the ventilation fan.The purpose is to match the air supply and air demand of the ventilation fan.