礦井通風阻力系數(shù)反演研究
發(fā)布時間：2018-07-23 08:37
【摘要】：礦井通風網(wǎng)絡解算的理論和算法研究早在70年代就已經(jīng)成熟,但時至今日通風網(wǎng)絡解算仍然沒有在礦井生產(chǎn)實際應用中得到廣泛的應用.通風網(wǎng)絡解算應用的3大瓶頸問題之一的通風阻力系數(shù)“測不準,總在變”的問題,時至今日該問題尚未解決,阻礙了通風網(wǎng)絡解算在實際中的應用.巷道風阻可通過經(jīng)驗公式計算和通風阻力測試獲取.風阻經(jīng)驗公式通常只是一些特殊情況的近似歸納,一些常量參數(shù)取值更多的依賴人為經(jīng)驗,存在較大的主觀性誤差；而現(xiàn)場測試工作量非常大且費時、費力.無論是經(jīng)驗公式還是現(xiàn)場測試,得到的風阻數(shù)據(jù)都存在誤差,使得仿真計算的結(jié)果與實際的通風系統(tǒng)不匹配.如何通過少量代表性巷道風量、節(jié)點壓力等有限的實測數(shù)據(jù),反演礦井通風系統(tǒng)阻力系數(shù),這是一項值得研究的課題.目前國內(nèi)外關(guān)于通風阻力系數(shù)反演研究較少,基于國家自然基金資助項目(60772159)《基于仿真技術(shù)的礦井通風系統(tǒng)智能診斷系統(tǒng)研究》,開展本文研究工作.從流體網(wǎng)絡三大基本定律出發(fā),建立通風阻力系數(shù)反演的矩陣方程組形式.無論是多測點一次觀測還是少測點多次觀測條件下,利用有限的巷道風量和節(jié)點壓力觀測數(shù)據(jù)來反演通風系統(tǒng)中的各條巷道風阻,由于方程數(shù)小于未知變量個數(shù),反演問題始終存在多解的情況,通風阻力系數(shù)反演問題是不適定的.基于最小二乘原理建立了通風阻力系數(shù)反演的數(shù)學模型,以實測壓力與與計算壓力的偏差以及實測風量與計算風量的偏差為目標函數(shù),綜合考慮了壓力、風量以及通風阻力系數(shù)范圍約束,通過該模型的建立將通風阻力系數(shù)反演問題轉(zhuǎn)化非線性優(yōu)化問題.采用遺傳算法和粒子群算法來求解基于最小二乘原理的通風阻力系數(shù)反演的優(yōu)化問題.針對通風阻力系數(shù)反演問題,對遺傳算法和粒子群算進行了改進,增強算法的全局搜索和局部搜索能力.上述研究基礎(chǔ)上,可以依據(jù)觀測點相對靈敏度選擇合適的觀測數(shù)據(jù)進行通風阻力系數(shù)反演.結(jié)合通風系統(tǒng)靈敏度理論和聚類分析理論,提出了一種基于反映通風系統(tǒng)阻力系數(shù)變化的巷道風量測點和節(jié)點壓力測點布置方法,對通風系統(tǒng)中可以觀測的巷道、節(jié)點進行分類,尋找少量代表性強的分支風量測點和節(jié)點壓力測量,最大可能的反映通風系統(tǒng)的實際運行狀態(tài),減少測試工作量.最后通過實例描述了基于粒子群算法的寺河礦二號井通風阻力系數(shù)反演過程,驗證了反演方法的可行性,為進一步的研究通風阻力系數(shù)反演問題以及實際工程應用奠定了基礎(chǔ),具有重要的指導意義.
[Abstract]:The theory and algorithm of mine ventilation network calculation have been mature since 1970s, but the ventilation network calculation has not been widely used in mine production. One of the three bottleneck problems in the application of ventilation network calculation is the problem of "uncertainty, total change" of ventilation resistance coefficient. Up to now, this problem has not been solved, which hinders the application of ventilation network calculation in practice. Roadway wind resistance can be calculated by empirical formula and ventilation resistance test. The empirical formula of wind resistance is usually an approximate induction of some special cases, and some constant parameters are more dependent on human experience, and there is a large subjective error, while the field test work is very heavy, time-consuming and laborious. No matter the empirical formula or the field test, there are errors in the wind resistance data, which makes the simulation results do not match the actual ventilation system. How to retrieve the resistance coefficient of mine ventilation system through a small amount of representative tunnel air volume, node pressure and other limited measured data is a subject worth studying. At present, there are few researches on the inversion of ventilation resistance coefficient at home and abroad. Based on the project funded by the National Natural Fund (60772159) < Research on Intelligent diagnosis system of Mine ventilation system based on Simulation Technology ", the research work is carried out in this paper. Based on the three basic laws of fluid network, the matrix equations of ventilation resistance coefficient inversion are established. Under the condition of multiple observation points or multiple observation points, the wind resistance of each tunnel in ventilation system can be retrieved by using the limited observation data of tunnel air volume and node pressure, because the number of equations is less than the number of unknown variables. The inversion problem always has multiple solutions, and the ventilation resistance coefficient inversion problem is ill-posed. Based on the least square principle, a mathematical model of ventilation resistance coefficient inversion is established. The deviation between measured and calculated pressure and between measured and calculated air volume is taken as the objective function, and the pressure is considered synthetically. Through the establishment of the model, the inversion problem of ventilation resistance coefficient is transformed into a nonlinear optimization problem. Genetic algorithm and particle swarm optimization algorithm are used to solve the optimization problem of ventilation resistance coefficient inversion based on least square principle. For the problem of ventilation resistance coefficient inversion, genetic algorithm and particle swarm optimization are improved to enhance the global and local search ability of the algorithm. Based on the above research, the ventilation resistance coefficient can be retrieved according to the relative sensitivity of observation points. Based on the sensitivity theory of ventilation system and the cluster analysis theory, a method of layout of air flow measurement points and nodal pressure measuring points of roadway based on the change of resistance coefficient of ventilation system is proposed. The nodes are classified to find a small number of representative branch air flow measurement points and node pressure measurement to reflect the actual operating state of the ventilation system and reduce the test workload. Finally, the inversion process of ventilation resistance coefficient of Sihe No. 2 well based on particle swarm optimization is described, which verifies the feasibility of the inversion method, and lays a foundation for further research on the inversion of ventilation resistance coefficient and its practical engineering application. It has important guiding significance.
【學位授予單位】：遼寧工程技術(shù)大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TD724
本文編號：2138830
[Abstract]:The theory and algorithm of mine ventilation network calculation have been mature since 1970s, but the ventilation network calculation has not been widely used in mine production. One of the three bottleneck problems in the application of ventilation network calculation is the problem of "uncertainty, total change" of ventilation resistance coefficient. Up to now, this problem has not been solved, which hinders the application of ventilation network calculation in practice. Roadway wind resistance can be calculated by empirical formula and ventilation resistance test. The empirical formula of wind resistance is usually an approximate induction of some special cases, and some constant parameters are more dependent on human experience, and there is a large subjective error, while the field test work is very heavy, time-consuming and laborious. No matter the empirical formula or the field test, there are errors in the wind resistance data, which makes the simulation results do not match the actual ventilation system. How to retrieve the resistance coefficient of mine ventilation system through a small amount of representative tunnel air volume, node pressure and other limited measured data is a subject worth studying. At present, there are few researches on the inversion of ventilation resistance coefficient at home and abroad. Based on the project funded by the National Natural Fund (60772159) < Research on Intelligent diagnosis system of Mine ventilation system based on Simulation Technology ", the research work is carried out in this paper. Based on the three basic laws of fluid network, the matrix equations of ventilation resistance coefficient inversion are established. Under the condition of multiple observation points or multiple observation points, the wind resistance of each tunnel in ventilation system can be retrieved by using the limited observation data of tunnel air volume and node pressure, because the number of equations is less than the number of unknown variables. The inversion problem always has multiple solutions, and the ventilation resistance coefficient inversion problem is ill-posed. Based on the least square principle, a mathematical model of ventilation resistance coefficient inversion is established. The deviation between measured and calculated pressure and between measured and calculated air volume is taken as the objective function, and the pressure is considered synthetically. Through the establishment of the model, the inversion problem of ventilation resistance coefficient is transformed into a nonlinear optimization problem. Genetic algorithm and particle swarm optimization algorithm are used to solve the optimization problem of ventilation resistance coefficient inversion based on least square principle. For the problem of ventilation resistance coefficient inversion, genetic algorithm and particle swarm optimization are improved to enhance the global and local search ability of the algorithm. Based on the above research, the ventilation resistance coefficient can be retrieved according to the relative sensitivity of observation points. Based on the sensitivity theory of ventilation system and the cluster analysis theory, a method of layout of air flow measurement points and nodal pressure measuring points of roadway based on the change of resistance coefficient of ventilation system is proposed. The nodes are classified to find a small number of representative branch air flow measurement points and node pressure measurement to reflect the actual operating state of the ventilation system and reduce the test workload. Finally, the inversion process of ventilation resistance coefficient of Sihe No. 2 well based on particle swarm optimization is described, which verifies the feasibility of the inversion method, and lays a foundation for further research on the inversion of ventilation resistance coefficient and its practical engineering application. It has important guiding significance.
【學位授予單位】：遼寧工程技術(shù)大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TD724
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