Research on intelligent linkage regulation and control of local ventilation in long distance heading face
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摘要: 现有长距离掘进通风调控研究多局限于局部通风机本身进行变频调风，少有针对长距离掘进工作面按需供风导向的研究。针对该问题，提出了一种长距离掘进工作面局部通风智能调控系统设计方案，该系统由井下监控系统、通风异常调控系统、地面工作站组成。井下监控系统通过对井下通风机、风筒和工作面进行实时监控，实现对局部通风机工作异常状态的研判预警、对风筒内部漏风情况的动态分析及对实际供需风量的动态预测。通风异常调控系统通过识别井下通风参数异常情况，制定不同参数不同等级的风险协同处置策略，实时显示掘进工作面瓦斯浓度等参数分布的具体情况。地面工作站挖掘工作面风流状态与风筒参数的潜在规律，形成风流−风筒调控模型，根据掘进工作面风流流动分布状态实现对风筒的实时调控；同时建立理论供风量、实际供风量、实际需风量相匹配的变频预测调风模型，基于变频调风模型和实时通风参数确定通风机运行频率，通过通风机智能变频实现按需供风，并在通风异常情况下，基于瓦斯涌出量预测和风排瓦斯极限能力辅以钻孔瓦斯抽采控制工作面瓦斯浓度，实现对长距离掘进工作面的通风安全保障。以转龙湾煤矿23303掘进工作面为例，对风流进行数值模拟，研究工作面风流分布状态，为掘进工作面风速传感器的布置调整提供依据。提出2种通风联动调控方式，即常态下变频调风和异常情况下调控排风：在常态下通过供需匹配分析确定通风机运行频率，实现通风机的智能变频调风；在通风异常情况下采用4种调控排风规则，保障长距离掘进工作面的通风安全，同时达到节能减排效果。构建了局部通风系统健康指标综合评价体系，通过综合评价模型和健康指数，实现对局部通风系统的实时健康“体检”，并定量显示不同指标的风险等级，确保局部通风系统处于健康状态。Abstract: The existing research on ventilation control for long-distance heading face is mostly limited to the frequency conversion of local ventilation fans themselves. There is few research on the on-demand wind supply direction for long-distance heading face. In order to solve the above problem, a design scheme for an intelligent regulation and control system for local ventilation in long-distance heading face is proposed. The system consists of an underground monitoring system, a ventilation anomaly control system, and a ground workstation. The underground monitoring system achieves early warning of abnormal working conditions of local ventilation fans, dynamic analysis of air leakage inside the air ducts, and dynamic prediction of actual supply and demand air volume by real-time monitoring of underground ventilation fans, air ducts, and working surfaces. The ventilation anomaly control system identifies underground abnormal ventilation parameters, develops risk collaborative disposal strategies for different parameters and levels, and displays the specific distribution of parameters such as gas concentration in the heading face in real-time. The ground workstation excavates the potential laws between the air flow status of the heading face and the parameters of the air duct, forming an air flow-air duct control model. Real time control of the air duct is achieved based on the distribution status of the air flow in the heading face. At the same time, the workstation establishes a variable frequency predictive air regulation model that matches the theoretical air supply, actual air supply, and actual air demand. Based on the variable frequency air regulation model and real-time ventilation parameters, the operating frequency of the fan is determined. The on-demand air supply is achieved through intelligent variable frequency of the fan. In abnormal ventilation situations, based on the prediction of gas emission and the limit capacity of air venting gas, supplemented by drilling gas extraction to control the concentration of gas in the working face, the ventilation safety guarantee for the long-distance heading face is achieved. Taking the 23303 heading face of Zhuanlongwan Coal Mine as an example, numerical simulation of air flow is conducted to study the distribution status of air flow in the face. It provides a basis for adjusting the layout of wind speed sensors in the heading face. The paper proposes two different ventilation linkage control methods, namely variable frequency wind regulation under normal conditions and regulating exhaust air under abnormal conditions. The operating frequency of the fan is determined through supply and demand matching analysis under normal conditions to achieve intelligent variable frequency air regulation of the fan. In case of abnormal ventilation, four regulation and exhaust rules are adopted to ensure the ventilation safety of long-distance heading faces, while achieving the effect of energy conservation and emission reduction. A comprehensive evaluation system for the health indicators of the local ventilation system has been constructed. Through a comprehensive evaluation model and health index, real-time health "physical examination" of the local ventilation system is achieved, and the risk levels of different indicators are quantitatively displayed to ensure that the local ventilation system is in a healthy state.
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图 1 局部通风智能调控系统组成
Figure 1. Composition of intelligent regulation and control system of local ventilation
图 6 井巷Y−Z水平截面风速云图
Figure 6. Air speed cloud diagram of Y-Z horizontal section in shaft
图 8 智能联动变频调风过程
Figure 8. The regulation of air volume by intelligent linkage and frequency conversion
图 11 局部通风系统健康指标综合评价体系
Figure 11. Comprehensive evaluation system of health indicators of local ventilation system
表 1 局部通风系统健康指数影响要素
Table 1. Factors influencing health index of local ventilation system
序号 影响要素 序号 影响要素 1 局部通风机运行稳定性 13 掘进工作面温度合格率 2 局部通风机综合效率 14 风量供需比 3 局部通风机无计划停风故障率 15 有效风量率 4 局部通风机安全装备合格率 16 防灾设施合格率 5 主备通风机切换合格度 17 通风设施合格率 6 风筒漏风率 18 参数监测故障率 7 风筒出口参数变化 19 风量调控装置失效概率 8 风筒破裂率 20 通风机监控系统误操作率 9 掘进工作面瓦斯超限频率 21 监测系统漏检率 10 掘进工作面风速合格率 22 局部通风安全投入 11 掘进工作面有害气体污染度 23 技术人员比例 12 掘进工作面粉尘超标率 24 通风相关专业比例 
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