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|Title:||Data-driven soft sensor approach for quality prediction in a refining process|
Partial least squares
|Citation:||Wang, D., Liu, J., Srinivasan, R. (2010-02). Data-driven soft sensor approach for quality prediction in a refining process. IEEE Transactions on Industrial Informatics 6 (1) : 11-17. ScholarBank@NUS Repository. https://doi.org/10.1109/TII.2009.2025124|
|Abstract:||In the petrochemical industry, the product quality reflects the commercial and operational performance of a manufacturing process. However, real-time measurement of product quality is generally difficult. Online prediction of quality using readily available, frequent process measurements would be beneficial in terms of operation and quality control. In this paper, a novel soft sensor technology based on partial least squares (PLS) regression is developed and applied to a refining process for quality prediction. The modeling process is described, with emphasis on data preprocessing, multivariate-outlier detection and variables selection. Enhancement of PLS strategy is also discussed for taking into account the dynamics in the process data. The proposed approach is applied to data from a refining process and the performance of the resulting soft sensor is evaluated by comparison with laboratory data and analyzer measurements. © 2009 IEEE.|
|Source Title:||IEEE Transactions on Industrial Informatics|
|Appears in Collections:||Staff Publications|
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