Dual-stream multidimensional network for aluminum electrolysis process superheat identification

Superheat identification is very important in aluminum electrolysis industry. In order to realize the real-time identification of superheat, this paper proposes a novel dual-stream multidimensional network (DM) model, which is composed of an efficient stream and an accuracy stream. In efficient stream, a mobilenet is equipped on Artificial Intelligence of Things (AIoT) devices to achieve quick superheat identification as normality or abnormality. In accuracy stream, a two-stream information sharing 3D residual network is equipped on a cloud computing service to achieve accurate identification through abnormal frames. Experiments are conducted on the aluminum electrolysis datasets, and the results verify that our DM model can realize efficient and accurate identification under resource saving condition.