基于遗传算法的铜闪速熔炼过程控制优化

Control optimization of copper flash smelting process based on genetic algorithms

基于已建立的铜闪速熔炼神经网络模型,以能耗费用最低为目标,在工艺指标控制范围内,采用遗传算法对铜闪速熔炼过程的工艺参数进行了仿真优化计算。结果表明,当空气、分配风、工艺氧和中央氧的市场价格折合比值分别为0.05、0.1、0.4和0.45,精矿量为128 t,其成分(质量分数)为Cu 20.61%、S 27.59%、Fe 24.72%、SiO2 11.64%和MgO 1.39%时,铜闪速熔炼工艺参数的遗传优化值为空气15 011 m3、分配风1 302 m3、工艺氧17 359 m3、中央氧1 000 m3、熔剂13.6 t;与实践平均值相比,若采用优化工艺参数控制,熔炼能耗费用可降低4.6%。

Based on the built neural network model, the technological parameters of copper flash smelting process were optimized to make energy consume the lowest by using genetic algorithms when the technological objects ranged in control scope. The simulation results show that the optimizing value of air is 15 011 m^3, distribution wind is 1 302 m^3, technological oxygen is 17 359 m^3, central oxygen is 1 000 m^3 and flux is 13.6 t, when the converted ratio of the marketable price of air is 0.05, distribution wind is 0.1, technological oxygen is 0.4, central oxygen is 0.45, and the concentrate mass is 128 t, the mass fractions of components of the concentrate are Cu 20.61%, S 27.59%, Fe 24.72%, SiO2 11.64% and MgO 1.39%, respectively. Compared with the practical average data, the energy consume can be reducod by 4.6% if the smelting process is controlled by adopting the optimizing technological parameters.