Next Article in Journal
A Compressed Sensing Based Method for Reducing the Sampling Time of A High Resolution Pressure Sensor Array System
Next Article in Special Issue
Alumina Concentration Detection Based on the Kernel Extreme Learning Machine
Previous Article in Journal
A Heterogeneous Sensing System-Based Method for Unmanned Aerial Vehicle Indoor Positioning
Previous Article in Special Issue
Just-in-Time Correntropy Soft Sensor with Noisy Data for Industrial Silicon Content Prediction
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(8), 1847; doi:10.3390/s17081847

A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces

1,2
,
1,2,* , 1,2
,
1,2
and
1,2
1
School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Received: 1 July 2017 / Revised: 4 August 2017 / Accepted: 7 August 2017 / Published: 10 August 2017
(This article belongs to the Special Issue Soft Sensors and Intelligent Algorithms for Data Fusion)
View Full-Text   |   Download PDF [3993 KB, uploaded 10 August 2017]   |  

Abstract

Gas utilization ratio (GUR) is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs). In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM) named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF), depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS) to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation. View Full-Text
Keywords: soft-sensor approach; data-driven model; machine learning; gas utilization ratio; blast furnace; online sequential extreme learning machine soft-sensor approach; data-driven model; machine learning; gas utilization ratio; blast furnace; online sequential extreme learning machine
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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Li, Y.; Zhang, S.; Yin, Y.; Xiao, W.; Zhang, J. A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces. Sensors 2017, 17, 1847.

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