Electrode regulating system modeling in electrical smelting furnace using recurrent neural network with attention mechanism

Abstract

Electrical smelting furnace (ESF) is the primary equipment to produce steer, nonferrous metals and other materials. In smelting process, it is beneficial to keep the smelting current stable within a reasonable range to improve product quality and reduce energy consumption. ESF is characterized by nonlinearity, strong coupling and time variation, which makes it difficult to establish accurate mathematical models. Therefore, we propose a data-driven recurrent neural network (RNN) using gated recurrent unit (GRU) with attention mechanism not only to establish the relationship between the electrode position and the smelting current but also to reveal the internal dynamic variation between three-phase currents for the electrode regulating system. Our proposed model is tested on the actual production data collected from a fused magnesium furnace (one kind of ESF) in Liaoning Province of China. Numerical results show RNN excels at processing sequential data and describing inner changes of the dynamic system; GRU alleviates long-term dependency problems in industrial big data; attention mechanism can identify and put emphasis on the crucial points containing key information in long sequential data. The results indicate that the proposed model is effective and feasible for the modeling of electrode regulating system.