基于改进GAAA算法的连采机外喷雾降尘参数优化
Optimizing Continuous Miner External Spray Parameter Based on Improved GAAA Algorithm

为提高连续采煤机外喷雾装置的降尘效率,改进安全技术和工作面环境,以雾化压力、喷雾有效作用区长度、喷雾扩散角、喷嘴直径、相邻喷雾截面圆重叠参数为设计变量,建立了降尘效率最大的目标函数,运用改进遗传算法和蚂蚁算法的混合算法(GAAA算法)对2~8 μm不同粒径粉尘的降尘效率进行整体参数优化,并对降尘效果进行了分析和模拟验证。研究表明,随粉尘粒径增加,平均降尘效率先增大后减小,耗水量逐渐增大,最优降尘参数组可使平均降尘效率达90.9%,提高了7.5%,耗水量减少了6.0%,其对煤矿井下安全事故的预防有重要意义。
In order to improve the efficiency of dust fall for continuous miner external spray, we ameliorate the safety engineering and working environment with spray pressure, effective length of spray, spray diffusion angle, nozzle diameter and overlay parameter of border upon section circle as design variables. Then we establish the maximum efficiency functions of dust fall as optimal objective, globally optimize the dust fall parameters for 2 to 8 μm with the genetic algorithm with ant algorithm(GAAA). Finally we conducted the computer simulation of the flow fields. The simulation results show that the average dust fall efficiency decreases after increase and that the water consumption gradually increases with the increasing size of dust. The optimal parameters make the average dust fall computational efficiency reach 90.9% and increase by 7.5%, and the water consumption decreases by 6.0%. All these have important significance for the preventing accidents of underground coal mining