A Multi-Branch Deep Feature Fusion Network with SAE for Rare Earth Extraction Process Simulation
Fangping Xu, Jianyong Zhu, Wei Wang · Processes · 2024
The Rare Earth Extraction Process (REEP) model is difficult to accurately establish via the extraction mechanism method due to its high complexity. This paper proposes a multi-branch deep feature fusion network with SAE (SAE-MBDFFN) for modeling REEP. We first design a neural network with a multi-branch output structure to simulate the cascade REEP by introducing a multiscale feature fusion mechanism, which can simultaneously concatenate hidden features, original features, and inter-branch coupling features. In order to deal with insufficient labeled data during model training, we then adopt a stacked Sparse Auto-Encoder (SAE) technology to extract the hidden information of mass unlabeled data based on unsupervised learning. This technology can determine the initial parameters of SAE-MBDFFN by unsupervised pretraining. The design methodology of the network is well-founded. Experiments on industrial data indicate that the proposed method has the lowest initial loss value and a faster convergence rate in the fine-tuning stage than other comparison methods, while the prediction accuracy is better well. These results show the effectiveness of the proposed method.