自适应FOA-SVR在铝电解槽出铝量预测中的应用

Application of Adaptive FOA-SVR in Aluminum Reduction Cell Output Prediction

为进行铝电解槽出铝量预测,提出了一种基于自适应FOA-SVR模型的铝电解槽出铝量预测方法。首先,设计了一种自适应果蝇优化算法,基于三维搜索空间引入自适应步长,并应用于支持向量回归模型参数优化。其次,选取槽平均电压、槽电阻、槽温度、加料量、氟盐量等5个影响铝电解槽出铝量的因素作为模型输入向量,对电解槽出铝量进行预测。最后,以某公司铝电解槽生产数据为例进行实验验证,自适应FOA-SVR模型预测结果相比FOA-SVR模型提高了预测精度和收敛效率,为电解铝产量预测提供了有效模型,具有一定的推广价值。

In order to predict the aluminum output of aluminum reduction cells,an aluminum output prediction method based on the adaptive FOA-SVR model is proposed.Primarily,an adaptive Drosophila optimization algorithm was designed,which intro⁃duced an adaptive step size based on a three-dimensional search space and applied it to parameter optimization of the support vec⁃tor regression model.Secondly,five factors that affect the aluminum output of the aluminum electrolysis cell are selected as model input vectors to predict the aluminum output of the electrolytic cell,which include the average cell voltage,cell resistance,cell temperature,feed amount,and fluoride salt amount.Finally,taking the production data of a certain company′s aluminum elec⁃trolysis cell as an example for experimental verification,compared with the FOA-SVR model,the prediction result shows that the prediction accuracy and convergence efficiency are improved with the adaptive FOA-SVR model,which provides an effective model for the prediction of electrolytic aluminum output with certain promotion value.