An ELM Based Online Soft Sensing Approach for Alumina Concentration Detection
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 th...
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2015-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/268132 |
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doaj-a11857a7981f40449d28799bdb962db42020-11-24T23:23:10ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/268132268132An ELM Based Online Soft Sensing Approach for Alumina Concentration DetectionSen Zhang0Xi Chen1Yixin Yin2School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaThe 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.http://dx.doi.org/10.1155/2015/268132 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sen Zhang Xi Chen Yixin Yin |
spellingShingle |
Sen Zhang Xi Chen Yixin Yin An ELM Based Online Soft Sensing Approach for Alumina Concentration Detection Mathematical Problems in Engineering |
author_facet |
Sen Zhang Xi Chen Yixin Yin |
author_sort |
Sen Zhang |
title |
An ELM Based Online Soft Sensing Approach for Alumina Concentration Detection |
title_short |
An ELM Based Online Soft Sensing Approach for Alumina Concentration Detection |
title_full |
An ELM Based Online Soft Sensing Approach for Alumina Concentration Detection |
title_fullStr |
An ELM Based Online Soft Sensing Approach for Alumina Concentration Detection |
title_full_unstemmed |
An ELM Based Online Soft Sensing Approach for Alumina Concentration Detection |
title_sort |
elm based online soft sensing approach for alumina concentration detection |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2015/268132 |
work_keys_str_mv |
AT senzhang anelmbasedonlinesoftsensingapproachforaluminaconcentrationdetection AT xichen anelmbasedonlinesoftsensingapproachforaluminaconcentrationdetection AT yixinyin anelmbasedonlinesoftsensingapproachforaluminaconcentrationdetection AT senzhang elmbasedonlinesoftsensingapproachforaluminaconcentrationdetection AT xichen elmbasedonlinesoftsensingapproachforaluminaconcentrationdetection AT yixinyin elmbasedonlinesoftsensingapproachforaluminaconcentrationdetection |
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