Next Article in Journal
Investigating the Influence of Temperature on the Kaolinite-Base Synthesis of Zeolite and Urease Immobilization for the Potential Fabrication of Electrochemical Urea Biosensors
Next Article in Special Issue
A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces
Previous Article in Journal
Detecting Unknown Artificial Urban Surface Materials Based on Spectral Dissimilarity Analysis
Previous Article in Special Issue
The Ship Movement Trajectory Prediction Algorithm Using Navigational Data Fusion
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(8), 1830; https://doi.org/10.3390/s17081830

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

1
Department of Electrical and Information Engineering, Shaoxing University, Shaoxing 312000, China
2
Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
*
Author to whom correspondence should be addressed.
Received: 28 May 2017 / Revised: 27 July 2017 / Accepted: 27 July 2017 / Published: 8 August 2017
(This article belongs to the Special Issue Soft Sensors and Intelligent Algorithms for Data Fusion)
Full-Text   |   PDF [1517 KB, uploaded 10 August 2017]   |  

Abstract

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. View Full-Text
Keywords: soft sensor; industrial blast furnace; silicon content; local learning; support vector regression; outlier detection soft sensor; industrial blast furnace; silicon content; local learning; support vector regression; outlier detection
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Chen, K.; Liang, Y.; Gao, Z.; Liu, Y. Just-in-Time Correntropy Soft Sensor with Noisy Data for Industrial Silicon Content Prediction. Sensors 2017, 17, 1830.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top