By Topic

Modeling Component Concentrations of Sodium Aluminate Solution Via Hammerstein Recurrent Neural Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Wei Wang ; State Key Lab. of Syhthetical Autom. for Process Ind., Northeastern Univ., Shenyang, China ; Tianyou Chai ; Wen Yu ; Hong Wang
more authors

The component concentrations of sodium aluminate solution are important indices in alumina processing. At present, they are obtained by laboratory titration on samples taken from the production process. Due to the delays in taking and testing samples, they cannot be used for real-time control and optimization. Existing online measurements are not adopted because of the characteristics of the sodium aluminate solution such as high viscosity and the ease of precipitation which leads to pipeline blocking and decreased precision. In this paper, a new modeling method is proposed to measure the component concentrations online using the measurements of conductivity and temperature. The method combines the partial least squares (PLS) technique and the Hammerstein recurrent neural networks (HRNN), where a stable learning algorithm with theoretical analysis is given for the HRNN model. For this PLS-based HRNN, the PLS technique is used to solve the high dimensional and correlated data. Meanwhile, the HRNN technique is used to fit the nonlinear and dynamic characters of the process. An industrial experimental study on a sodium aluminate solution is described. The experiment results show that the proposed method is sufficient to warrant further evaluation in industrial scale experiments.

Published in:

Control Systems Technology, IEEE Transactions on  (Volume:20 ,  Issue: 4 )