基于极限学习机的铝电解过程参数软测量  

Soft measurement of parameters of aluminum electrolysis process based on ELM

作　　者：韩婷 贠卫国[1] HAN Ting;YUN Weiguo(College of Information and Control Engineering,Xi’an University of Architecture&Technology,Xi’an 710055,China)

机构地区：[1]西安建筑科技大学信息与控制工程学院,陕西西安710055

出　　处：《传感器与微系统》2020年第9期132-134,137,共4页Transducer and Microsystem Technologies

摘　　要：针对铝电解过程参数在线或快速检测难的问题,基于500kA预焙铝电解槽生产数据,提出粒子群优化的在线极限学习机软测量模型。采用在线贯序极限学习机(OS-ELM)增强对系统动态跟踪能力,同时利用粒子群算法优化极限学习机的结构,以达到减少随机参数误差的目的,并在速度更新公式中加入动态的惯性权值和学习因子来平衡全局搜索和局部搜索能力,避免种群陷入早熟收敛。实验结果验证该方法对氧化铝浓度及电解质温度的拟合度较好,对确保铝电解过程物料平衡及热平衡具有重要意义。Aiming at the problem that it is difficult to online or rapidly detect on aluminum electrolysis process parameters,based on the production data of the 500 k A prebaked aluminum reduction cell,a soft sensor model based on particle swarm optimization online extreme learning machine is proposed.The online sequential extreme learning machine(OS-ELM)is used to enhance the dynamic tracking capability of the system.Moreover,The particle swarm optimization algorithm is employed to find the optimal values of input weight and hidden layer bias in the network of extreme learning machine,so as to reduce the stochastic error.Furthermore,it is easy to fall into premature convergence phenomenon.Hence dynamic decay weights and learning factors are added to the speed update formula to balance global search and local search capabilities.Experimental results show that the proposed soft-sensor model has higher prediction accuracy,which is of great significance to ensure material balance and heat balance in aluminum electrolysis process.

关 键 词：氧化铝浓度 电解质温度 软测量 在线极限学习机 粒子群优化算法 

分 类 号：TF821[冶金工程—有色金属冶金]