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Sensors 2017, 17(9), 2002; doi:10.3390/s17092002

Alumina Concentration Detection Based on the Kernel Extreme Learning Machine

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: 10 July 2017 / Revised: 27 August 2017 / Accepted: 28 August 2017 / Published: 1 September 2017
(This article belongs to the Special Issue Soft Sensors and Intelligent Algorithms for Data Fusion)
View Full-Text   |   Download PDF [1611 KB, uploaded 1 September 2017]   |  

Abstract

The concentration of alumina in the electrolyte is of great significance during the production of aluminum. The amount of the alumina concentration may lead to unbalanced material distribution and low production efficiency and affect the stability of the aluminum reduction cell and current efficiency. The existing methods cannot meet the needs for online measurement because industrial aluminum electrolysis has the characteristics of high temperature, strong magnetic field, coupled parameters, and high nonlinearity. Currently, there are no sensors or equipment that can detect the alumina concentration on line. Most companies acquire the alumina concentration from the electrolyte samples which are analyzed through an X-ray fluorescence spectrometer. To solve the problem, the paper proposes a soft sensing model based on a kernel extreme learning machine algorithm that takes the kernel function into the extreme learning machine. K-fold cross validation is used to estimate the generalization error. The proposed soft sensing algorithm can detect alumina concentration by the electrical signals such as voltages and currents of the anode rods. The predicted results show that the proposed approach can give more accurate estimations of alumina concentration with faster learning speed compared with the other methods such as the basic ELM, BP, and SVM. View Full-Text
Keywords: aluminum electrolysis; alumina concentration; extreme learning machine; kernel extreme learning machine; K-fold cross validation; predict aluminum electrolysis; alumina concentration; extreme learning machine; kernel extreme learning machine; K-fold cross validation; predict
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Zhang, S.; Zhang, T.; Yin, Y.; Xiao, W. Alumina Concentration Detection Based on the Kernel Extreme Learning Machine. Sensors 2017, 17, 2002.

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