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Mathematical Problems in Engineering
Volume 2015, Article ID 268132, 8 pages
Research Article

An ELM Based Online Soft Sensing Approach for Alumina Concentration Detection

School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Received 19 August 2014; Accepted 30 October 2014

Academic Editor: Yi Jin

Copyright © 2015 Sen Zhang et al. 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.


The concentration of alumina in the electrolyte is of great significance during the production of aluminum; it may affect the stability of aluminum reduction cell and the current efficiency. However, the concentration of alumina is hard to be detected online because of the special circumstance in the aluminum reduction cell. At present, there is lack of fast and accurate soft sensing methods for alumina concentration and existing methods can not meet the needs for online measurement. In this paper, a novel soft sensing method based on a modified extreme learning machine (MELM) for online measurement of the alumina concentration is proposed. The modified ELM algorithm is based on the enhanced random search which is called incremental extreme learning machine in some references. It randomly chooses the input weights and analytically determines the output weights without manual intervention. The simulation results show that the approach can give more accurate estimations of alumina concentration with faster learning speed compared with other methods such as BP and SVM.