Study on intelligent ventilation linkage control theory and supply–demand matching experiment in mines
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摘要: 鑒于礦井通風系統動態匹配自動化調節的現場需求，分析了風量供需匹配原理與聯動調控方法，建立了多元特征融合的主通風機調頻、關聯分支調阻及聯合調節的數學模型。提出了通風網絡分支供需匹配調控模型和穩定性判定方法，基于有毒有害氣體涌出（排放）預測的需風模型，開發了礦井通風供需偏離的智能化應急調控軟件。實現了通風供需失衡選擇變頻調節時，自動計算通風機最佳工作頻率；選擇關聯分支風阻調節時，運用元胞自動機模型計算出最佳調節巷道，并通過風網反演計算模型獲取調節風阻值；當單一調節方式失效時，生成風機變頻與分支調阻聯合調控方案；通過風網超前模擬分析實現風量供需匹配的可靠調節。運用典型礦井通風系統建立了風網分支需風量自動化調節實驗模型，以現場有毒有害氣體超限統計規律為分支需風量調控導向模型開展調風稀釋實驗，結果表明：三種調節方式下分支風量嚴格按照調控理論模型變化，調風過程中CO2濃度變化延時明顯，風機變頻調節的風網波動較小，分支風阻調節對局部風網影響大，聯合調節風網波動性大。實驗驗證了礦井通風供需匹配智能化調控系統的實用性和可行性，為礦井通風聯動調控提供理論和應用指導。Abstract: To determine the dynamic matching of a mine ventilation system to onsite demands of automatic adjustment, we analyze the principle of air volume supply and demand matching and a linkage control method. Subsequently, we establish a mathematical model of main ventilator frequency adjustment, associate branch resistance adjustment, and joint adjustment with multi-feature fusion. We also propose a matching regulation model and a stability determination method for a ventilation network’s branch supply and demand. Based on the monitoring of harmful gases, intelligent emergency control software is developed by a mine ventilation supply and demand model. We realize the automatic calculation of the best working frequency of a ventilator when an unbalanced supply and ventilation demand is selected for frequency conversion adjustment. When selecting the associated branch wind resistance adjustment, we use a cellular automata model to calculate the optimal adjustment roadway. We obtain the adjusted wind resistance value using a winding network inversion calculation model. When a single adjustment method fails, a joint control scheme of fan frequency conversion and branch resistance adjustment is generated. A reliable adjustment of air volume supply and demand matching is realized through an advanced simulation analysis of the air network. A typical mine ventilation system is used to establish an experimental model for the automatic adjustment of the air demand of a branch of a winding network. The air demand adjustment and dilution experiment are carried out with the statistical law of onsite gas overrun as the guidance model of branch air demand control. The following results are obtained. The branch air volume changes according to the adjustment theory model under three adjustment methods. Further, the CO2 concentration change is evidently delayed in the air adjustment process. In the process of fan frequency conversion regulation, the air volume of each branch of the air network changes according to the ventilation network sensitivity, and the fluctuation of the air network is minimal. When a single associated branch resistance adjustment method is used to regulate the wind, the local wind network has great influence on air volume and thus fluctuates greatly. When the fan frequency and associated branch wind resistance are combined, the fluctuation of the branch air volume of the entire air network is the largest, and the system stability and security are the lowest. Therefore, the fan frequency and combined regulation methods of multiple associated branches are recommended to use in practical applications of mines. The experiment verified the practicability and feasibility of the deviation of mine ventilation supply and demand from intelligent control systems. It also provided theoretical and application guidance for mine ventilation linkage control.
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圖 1 實驗模型系統的通風網絡圖
Figure 1. Ventilation network diagram of the experimental model system
圖 2 關聯分支隨L5風阻調節的風量變化規律. （a）主要分支風量變化規律；（b）工作面風量變化特性曲線
Figure 2. Variation laws of the air volume of an associated branch adjusted with L5 wind resistance: (a) variation law of air volume in main branches and (b) characteristic curve of air volume change in the mining face
圖 3 關聯分支隨L10調節的風量變化規律. （a）主要分支風量變化規律；（b）工作面風量變化特性曲線
Figure 3. Variation law of associated branch’s air volume adjusted with L10: (a) variation law of air volume in main branches and (b) characteristic curve of air volume change in the mining face
圖 4 瓦斯異常增風稀釋變頻調風流程圖
Figure 4. Flow chart of the variable frequency to adjust the air volume of a branch
圖 5 礦井通風供需匹配智能化調控原理
Figure 5. Intelligent regulation principle of mine ventilation supply and demand matching
圖 6 礦井通風智能化調控實驗平臺. （a）實驗模型實物圖；（b）實驗模型三維立體圖
Figure 6. Experimental platform of mine ventilation intelligent control: (a) real image of the experimental model and (b) 3D stereogram of the experimental model
圖 7 通風供需失衡的自動化調控平臺
Figure 7. Automatic control platform of the ventilation supply and demand imbalance
圖 8 瓦斯異常涌出超限特性曲線圖. （a）陽泉某礦工作面瓦斯濃度超限特性；（b）某礦綜采工作面瓦斯超限數據（周期來壓時）
Figure 8. Characteristic curves of volume fraction of gas overrun caused by abnormal emission: (a) overlimit characteristics of volume fraction of gas in a mining face of a mine in Yangquan; (b) gas overload data of a fully mechanized mining face of a mine (periodic pressure)
圖 9 瓦斯異常涌出時通風機變頻調風稀釋實驗
Figure 9. Air conditioning dilution experiment on fan frequency conversion during abnormal gas emission
圖 10 瓦斯異常涌出時關聯分支風阻調節增風實驗
Figure 10. Associated branch wind resistance adjustment and wind increase experiment during abnormal gas emission
圖 11 瓦斯異常涌出時聯合調節增風實驗
Figure 11. Combined regulation of a wind increase experiment in abnormal gas emission
表 1 通風網絡調節分支的元胞自動機研判結果
Table 1. Analysis results of cellular automata of a ventilation network regulation branch
Branch number L1 L2 L3 L4 L5 L6 L7 L8 L9 L 10 L11 L12 L13 L14 L15 L16 L17 L18 L19 L20 L21 Weight of adjustment 0 0 0 0 1 Start 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 
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