矿井通风网络风量智能调控研究

Research on intelligent control of air volume of mine ventilation network

  • 摘要: 现有矿井通风网络风量智能优化算法在求解调风参数时普遍存在模型复杂、收敛速度慢、易陷入局部最优等缺陷,同时也缺乏与调风分支优化选择相结合的研究。针对上述问题,提出了一种基于改进天牛须搜索(BAS)算法的矿井通风网络风量智能调控方法。首先,以用风分支的风量需求为优化目标,构建风量优化调节数学模型,针对该模型中的风量调节约束条件,采用不可微精确罚函数并结合模拟退火算法优化惩罚项,实现模型的去约束化。然后,通过求解灵敏度矩阵,结合风量灵敏度和分支支配度理论选择最优的调节分支集,确定其风阻调节范围,并作为模型的初始解集。最后,基于改进BAS算法求解出最优调风参数,进而控制对应的调风设施,实现风量调控。基于矿井通风实验平台对该方法的可靠性进行实验验证,结果表明:相比于标准BAS算法和粒子群优化(PSO)算法,改进BAS算法综合寻优性能更优越,解得的风量平均值和最优解均高于PSO算法和标准BAS算法,平均运行时间虽略长于标准BAS算法,但远短于PSO算法,平均收敛代数最多,精度最高,容易跳出局部循环得到最优解;在设定风量调节目标后,基于改进BAS算法的矿井通风网络风量智能调控方法可快速精准求解出待调分支的风量最优值,调节后的分支风量满足矿井安全生产的调风要求,风量上调高达 46.5%。

     

    Abstract: The existing intelligent optimization algorithm of air volume of mine ventilation network has the defects of complex model, slow convergence speed, easy falling into local optimum when solving the air adjustment parameters. There is a lack of research on the combination of optimal selection of air adjustment branches. To solve the above problems, an intelligent control method of air volume of mine ventilation network based on improved beetle antennae search (BAS) algorithm is proposed. Firstly, the mathematical model of air volume optimal adjustment is established by taking the air volume demand of the air consumption branch as the optimization objective. In view of the air volume adjustment constraint conditions in the model, the non-differentiable exact penalty function and the simulated annealing algorithm are adopted to optimize the penalty term, so that the model is unconstrained. Secondly, by solving the sensitivity matrix and combining the theory of air volume sensitivity and branch dominance, the optimal adjustment branch set is selected. The air resistance adjustment range is determined as the initial solution set of the model. Finally, based on the improved BAS algorithm, the optimal air adjustment parameters are solved. The corresponding air adjustment facilities are controlled to realize air volume adjustment. The reliability of the method is verified by experiments based on the mine ventilation experimental platform. The results show that compared with the standard BAS algorithm and particle swarm optimization (PSO) algorithm, the improved BAS algorithm has superior comprehensive optimization performance. The average value and optimal solution of air volume are higher than those of the PSO algorithm and standard BAS algorithm. Although the average running time is slightly longer than the standard BAS algorithm, it is far shorter than the PSO algorithm. The average convergence algebra is the most, the precision is the highest, and it is easy to jump out of the local loop to get the optimal solution. After setting the air volume adjustment target, the intelligent control method of air volume of the mine ventilation network based on the improved BAS algorithm can quickly and accurately solve the optimal value of the air volume of the branch to be adjusted. The adjusted branch air volume meets the air volume adjustment requirements of mine safety production, and the air volume is increased up to 46.5%.

     

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