铝电解过程电流效率智能集成预测模型

Intelligent Integrated Prediction Model of Current Efficiency of the Aluminum Reduction Process

针对铝电解过程电流效率预测问题,建立了一种电流效率智能集成预测模型。首先,基于铝电解过程及其数据的特点,以及模糊c-均值聚类和非监督聚类方法的不足,提出一种模糊c-均值监督聚类改进算法对其聚类,并在此基础上建立了监督式分布支持向量机智能预测模型。其次,基于铝电解反应原理,建立了电流效率机理预测模型。最后,对两种模型进行加权集成,得到电流效率智能集成模型,并利用现场生产数据进行仿真验证,结果表明其预测精度较高,可用于铝电解电流效率实际生产预报。

To predict the current efficiency of the aluminum reduction process, an intelligent integrated prediction model is presented. Firstly, in view of the deficiency of the fuzzy c-means clustering and unsupervised clustering method, an improved fuzzy c-means supervised clustering algorithm is proposed and on the basis of it, a supervised distributed support vector machine is constructed for the prediction of the current efficiency. Secondly, based on the aluminum electrolytic reaction principle, the mechanism model of current efficiency is established. Finally, by the weighted integration of the two models, the intelligent integrated model of current efficiency is achieved. The simulation results of the field data show that the integrated model has higher precision, and can be used to forecast the aluminum electrolysis current efficiency in practice.