| With the gradual depletion of shallow resources,in order to maintain the rapid development of the national economy,the mining of metal minerals has entered the deep earth,which increases the complexity of the ventilation network and makes it more difficult to achieve effective air distribution on demand,resulting in increased risk of underground personnel operations.With the advancement of intelligent mining,it is urgent to study the theory and technology of intelligent control of on-demand air supply in deep roadways.A metal mine in Yunnan is a representative deep underground mine.Based on this,this paper takes it as the research object,and uses graph theory,network solution method,intelligent optimization algorithm and other means to study its resistance intelligent optimization.The work done is as follows:(1)Through on-site investigation and analysis,it is determined that the current network does not have resistance optimization.The calculation results show that the main fan air volume adjustment range is 105m~3/s to 185m~3/s,and the calculated air volume is 142.56m~3/s within the fan air volume adjustment range.The network has resistance optimization.In the natural state,40%of the distribution component of the wind branch of the network does not meet the calculated air volume,and the resistance optimization is needed to realize the air distribution on demand.(2)Taking the minimum energy consumption of the main fan as the optimization objective function,the upper and lower limits of branch air volume,fan efficiency,fan working air pressure,air volume balance law,air pressure balance law and resistance law as constraints,the ventilation resistance optimization model is constructed,which is the preliminary work of intelligent optimization of ventilation resistance.(3)Taking QPSO intelligent optimization algorithm as the mathematical model solution method,the idea and process of intelligent optimization algorithm to realize intelligent optimization of ventilation resistance are analyzed.It is determined that the implementation of intelligent optimization of ventilation resistance needs to combine intelligent optimization algorithm with network settlement method.Therefore,QPSO algorithm is combined with Scott-Honsley method to construct QPSO ventilation resistance intelligent optimization model,and the pseudo code is given.(4)The refitted ventilation network parameters are brought into the QPSO ventilation resistance intelligent optimization model,and a set of optimal resistance optimization schemes are obtained.The implementation results show that the air volume of the main fan is 157.9m~3/s,which meets the calculated air volume of142.56m~3/s.The supply and demand ratio of air volume in 15 wind sites is greater than100%.The model overcomes the defects of complex deep ventilation network,high correlation between points,and difficulty in realizing on-demand air distribution adjustment,and realizes effective on-demand air distribution in deep roadways.(5)The reliability of fan,branch and air network before and after intelligent optimization of resistance is calculated by reliability engineering theory.The results show that the reliability of main fan is 0.0456 and 1 respectively.The number of branches with reliability greater than 0.90 was 9.52%and 85.71%,respectively.The reliability of the wind network is 0 and 0.9178 respectively.The resistance optimization scheme obtained by the model can make the whole ventilation system safer and more reliable.Therefore,the QPSO ventilation resistance intelligent optimization program studied in this paper provides a reasonable,effective and reliable resistance optimization implementation plan for the ventilation resistance optimization of a metal mine in Yunnan,and realizes the effective air distribution on demand in complex deep wells.It provides a new intelligent control technology to solve the problem that the ventilation network of deep roadway is complex and it is difficult to realize effective air distribution adjustment on demand. |
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