Deep learning with neighborhood preserving embedding regularization and its application for soft sensor in an industrial hydrocracking process

Abstract

Recently, deep learning has attracted increasing attention for soft sensor applications in industrial processes. Hierarchical features can be learned from massive process data by deep learning, which is the key step for quality variable prediction. However, few deep learning algorithms consider the neighborhood structure of data samples for feature extraction in industrial processes. In this paper, a novel stacked neighborhood preserving autoencoder (S-NPAE) is proposed to extract hierarchical neighborhood-preserving features. As for each NPAE, a novel loss function is proposed to reconstruct the input data and preserve the neighborhood structure of the input data simultaneously. By minimizing this loss function, NPAE can efficiently extract the neighborhood-preserved features from its input data. Then, the deep S-NPAE network is constructed by stacking multiple NPAEs in a hierarchical way. Finally, the extracted features can be used for accurate quality prediction in soft sensor modeling. The experimental results on an industrial hydrocracking process demonstrate the effectiveness of the proposed method when compared with other commonly used methods.