基于改进多重极限学习机的槽电压优化方法  

Optimization Method of the Cell Voltage Based on The Improved Multiple Extreme Learning Machine

作　　者：徐辰华[1] 平嘉鸣 林小峰[1] 黄清宝[1] 李智[1] XU Chen-hua;PING Jia-min;LIN Xiao-feng;HUANG Qing-bao;LI Zhi(School of Electrical Engineering,Guangxi University,Nanning 530004,China)

机构地区：[1]广西大学电气工程学院,广西南宁530004

出　　处：《控制工程》2020年第4期758-764,共7页Control Engineering of China

基　　金：国家自然科学基金(61650302)。

摘　　要：为了降低电解铝生产的电能消耗,提出了一种基于遗传算法的多重极限学习机(Genetic Algorithm-Multiple Extreme Learning Machine, GA-MELM)的电解铝生产过程槽电压优化方法,以寻找最优的生产槽电压和对应的生产条件。首先采用核主元分析法确定影响电解槽槽电压的关键参数,建立槽电压的多重极限学习机软测量模型。然后利用遗传算法对电解铝槽电压模型进行寻优,获得槽电压优化值和对应的优化操作参数。通过实际的生产数据进行仿真实验,结果表明,采用GA-MELM方法能够较准确的对电解铝槽电压进行预测和优化,从而为实际电解铝生产降低能耗提供有效的理论指导。In order to reduce the production cost of electrolytic aluminum,an optimization extreme method is proposed based on genetic algorithm-multiple extreme learning machine(GA-MELM)to find the optimal production cell voltage and the corresponding production conditions.First,kernel principal component analysis method is used to determine the key parameters that affect aluminum electrolysis production,and the MELM model of electrolytic aluminum is established.Then the genetic algorithm is used to optimize the electrolytic aluminum channel voltage model and obtain the optimized cell voltage and corresponding operation parameters.The simulation experiment struck by the actual production data,the results show that the GA-MELM can forecast and optimize the electrolysis cell voltage,and then it can provide effective theoretical guidance to reduce energy consumption for practical production process.

关 键 词：电解铝 槽电压 多重极限学习机 遗传算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]