Just-in-Time Correntropy Soft Sensor with Noisy Data for Industrial Silicon Content Prediction

Sensors

Kun Chen¹  Yi Liu²  Zengliang Gao²  Yu Liang² 

[1] Department of Electrical and Information Engineering, Shaoxing University, Shaoxing 312000, China;Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China;

关键词: soft sensor;    industrial blast furnace;    silicon content;    local learning;    support vector regression;    outlier detection;   

DOI  :  10.3390/s17081830

来源: DOAJ

【 摘 要 】

Development of accurate data-driven quality prediction models for industrial blast furnaces encounters several challenges mainly because the collected data are nonlinear, non-Gaussian, and uneven distributed. A just-in-time correntropy-based local soft sensing approach is presented to predict the silicon content in this work. Without cumbersome efforts for outlier detection, a correntropy support vector regression (CSVR) modeling framework is proposed to deal with the soft sensor development and outlier detection simultaneously. Moreover, with a continuous updating database and a clustering strategy, a just-in-time CSVR (JCSVR) method is developed. Consequently, more accurate prediction and efficient implementations of JCSVR can be achieved. Better prediction performance of JCSVR is validated on the online silicon content prediction, compared with traditional soft sensors.

【 授权许可】

Unknown