Stacked Enhanced Auto-Encoder for Data-Driven Soft Sensing of Quality Variable

Data-driven soft sensors have been widely used in industrial processes. Traditional soft sensors are mostly shallow networks, which cannot easily describe the complicated process data patterns. In this article, a new deep learning approach is proposed for soft sensors, which is based on the stacked enhanced auto-encoder (SEAE). The original stacked auto-encoder (SAE) learns the hierarchical features of the raw observed input data with unsupervised layerwise pretraining. In each layer, the features are learned from its low-level ones with an AE by minimizing the reconstruction error for them. This usually causes accumulation of information loss from the lowest layer to the highest layer. Hence, the learned features may not well represent the raw input data patterns. The new SEAE network is designed by adding the constraint of additional reconstruction for the raw input data at each layer. In this way, the learned features at each layer are a good representation for the raw input data. The effectiveness of the proposed SEAE-based soft sensor method is validated on an industrial sulfur recovery unit.