A nonlinear industrial soft sensor modeling method based on locality preserving stochastic configuration network with utilizing unlabeled samples

Zhao, Yue; Deng, Xiaogang; Li, Sen

doi:10.1016/j.isatra.2023.04.012

Cited by 10 publications

(1 citation statement)

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“…Making sure that these models are reliable under changing operating conditions, dealing with data security issues, and avoiding the risks of model overfitting are areas needing urgent attention [ 29 , 30 ]. In addition, the successful use of these technologies needs careful planning regarding data handling, feature selection, and effective model validation techniques [ 31 , 32 , 33 ]. Dealing with these challenges demands a deeper understanding of both soft sensor design and the underlying deep learning mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Soft Sensing of LPG Processes Using Deep Learning

Sifakis,

Sarantinoudis,

Tsinarakis

et al. 2023

Sensors

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This study investigates the integration of soft sensors and deep learning in the oil-refinery industry to improve monitoring efficiency and predictive accuracy in complex industrial processes, particularly de-ethanization and debutanization. Soft sensor models were developed to estimate critical variables such as the C2 and C5 contents in liquefied petroleum gas (LPG) after distillation and the energy consumption of distillation columns. The refinery’s LPG purification process relies on periodic sampling and laboratory analysis to maintain product specifications. The models were tested using data from actual refinery operations, addressing challenges such as scalability and handling dirty data. Two deep learning models, an artificial neural network (ANN) soft sensor model and an ensemble random forest regressor (RFR) model, were developed. This study emphasizes model interpretability and the potential for real-time updating or online learning. The study also proposes a comprehensive, iterative solution for predicting and optimizing component concentrations within a dual-column distillation system, highlighting its high applicability and potential for replication in similar industrial scenarios.

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