Multi-Gate Mixture-of-Experts Stacked Autoencoders for Quality Prediction in Blast Furnace Ironmaking

The blast furnace is an energy-intensive and extremely complex reactor in the ironmaking process. To reduce energy consumption, improve product quality, and ensure the stability of blast furnace operation, it is very important to predict the quality indicators of molten iron accurately and in real time. However, most of the existing product quality prediction models, such as the stacked autoencoder (SAE) model, use a single-channel stack structure. For such models, when the working conditions of the blast furnace ironmaking process change, a large prediction error will occur. To solve this issue, this paper develops a novel deep learning model, called the multi-gate mixture-of-experts stacked autoencoder (MMoE-SAE), for predicting the quality variable in the blast furnace ironmaking processes. The proposed MMoE-SAE model is constructed based on a multi-gate hybrid expert structure, in which a series of SAE networks are selected as experts. The MMoE-SAE model inherits the advantages of MMoE and SAE, which can not only extract the deep features of the data but also have better adaptability to the changes of working conditions in the blast furnace ironmaking process. To verify the effectiveness and practicability of the proposed MMoE-SAE model, it was applied to predict the silicon content of molten iron in the blast furnace ironmaking process. The experimental results demonstrate that the proposed MMoE-SAE model outperforms other prediction models in prediction accuracy.