Novel semi-supervised sparse stacked autoencoder integrated with local linear embedding for industrial soft sensing

Data-driven industrial soft sensor modeling techniques have been widely applied in predicting key variables in complex industrial processes. However, with industrial processes becoming increasingly intricate, the data they produce exhibit characteristics such as strong temporal dependencies, high dimensionality, and local structures, posing significant challenges for soft sensing. To address these issues, this paper proposes novel Semi-Supervised Sparse Stacked Autoencoder integrated with the Local Linear Embedding algorithm (SS-SAE-LLE). Unlike traditional autoencoders (AE), which capture hierarchical data features by minimizing global fitting error, SS-SAE-LLE simultaneously accounts for the spatio-temporal characteristics of the data through the local linear embedding algorithm. Moreover, it incorporates supervised tuning by leveraging labeled data and training with a semi-supervised learning framework, further improving prediction accuracy. To evaluate the feasibility of the proposed method, experiments are conducted on PTA solvent and SRU system datasets. The simulation results demonstrate that SS-SAE-LLE achieves higher prediction accuracy than other models, highlighting its applicability in the field of industrial soft sensor modeling.