In the traditional production of electrolytic aluminum industry,the production decisions of aluminum electrolytic cells are usually made based on the multi-years experience of process technicians.The aluminum output is an important decision variable with strong coupling,and the quality of its de-cision-making has a direct and important impact on the production stability and efficiency of the electrolytic cell.This paper proposes a Transformer ar-chitecture model FDisformer that can deeply mine data frequency features and feature filtering.It optimizes the encoding layer of traditional Transformer,thereby mining data trend changes and deeper feature information.At the same time,a feature distillation module is introduced to ensure the screening of features strongly related to the task of aluminum production decision-making.FDisformer has higher performance indicators in aluminum output decision-making,and the establishment of this model can provide a reference basis for the subsequent daily aluminum output decision-making in aluminum e-lectrolytic cells.
维普
万方数据