A new soft sensor method for dynamic processes based on dynamic orthogonal forward regression

To cope with the issue of dynamic characteristic in industrial processes, a new soft sensor method based on dynamic orthogonal forward regression (dynamic OFR) is proposed in this paper. The proposed method applies OFR to the augmented matrix with time-delayed secondary variables. The meaningful tim...

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Bibliographic Details
Published inThe 27th Chinese Control and Decision Conference (2015 CCDC) pp. 536 - 541
Main Authors Ruan Hongmei, Tian Xuemin, Cai Lianfang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2015
Subjects
Accuracy
Computational modeling
Correlation
Delays
Dynamic
Input variables
Orthogonal forward regression
Predictive models
Process control
Soft sensor
Time-delayed
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Summary:To cope with the issue of dynamic characteristic in industrial processes, a new soft sensor method based on dynamic orthogonal forward regression (dynamic OFR) is proposed in this paper. The proposed method applies OFR to the augmented matrix with time-delayed secondary variables. The meaningful time-delayed variables which can well explain primary variables are then selected automatically and a sparse soft sensor model is thus constructed. The simulation results on predicting butane concentration in the bottom of debutanizer column demonstrate the superiority of the proposed method in terms of prediction accuracy and the computational complexity.
ISSN:1948-9439
1948-9447
DOI:10.1109/CCDC.2015.7161750