(1. 江西理工大学 材料与化学工程学院，赣州 341000；
2. 中南大学 冶金科学与工程学院，长沙 410083；
3. 江西理工大学 信息工程学院，赣州 341000)
摘 要: 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%。
关键字: 铜闪速熔炼；神经网络；遗传算法；控制优化
based on genetic algorithms
（1. Faculty of Material and Chemistry Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China;
2. School of Metallurgical Science and Engineering, Central South University,Changsha 410083, China;
3. Faculty of Information Engineering,Jiangxi University of Science and Technology, Ganzhou 341000, China）
Abstract: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 m3, distribution wind is 1 302 m3, technological oxygen is 17 359 m3, central oxygen is 1 000 m3 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 reduced by 4.6% if the smelting process is controlled by adopting the optimizing technological parameters.
Key words: copper flash smelting; neural network; genetic algorithms; control optimization
