Estimation of component concentrations of sodium aluminate solution via PLS and Hammerstein recurrent neural networks

Author

Wang, Wei ; Zhao, Lijie ; Chai, Tianyou ; Yu, Wen

Author_Institution

Key Lab. of Integrated Autom. of Process Ind., Northeastern Univ., Shenyang, China

fYear

2010

fDate

25-27 Aug. 2010

Firstpage

107

Lastpage

111

Abstract

In this paper, a new on-line soft sensing method is proposed for component concentrations of sodium aluminate solution. With this sensing strategy, real-time control and optimization can be realized in aluminate production plants. Several advance techniques are used, such as PLS (Partial Least Squares), Hammerstein model, recurrent neural networks and least square algorithm. Industrial experiment results show that the proposed soft sensing algorithm is effective.

Keywords

Internet; chemistry computing; least squares approximations; recurrent neural nets; Hammerstein recurrent neural network; PLS; aluminate production plant; component concentration estimation; online soft sensing method; partial least square algorithm; real-time control; sodium aluminate solution; Artificial neural networks; Computational modeling; Data models; Heuristic algorithms; Nonlinear dynamical systems; Recurrent neural networks; Temperature measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computational Intelligence (IWACI), 2010 Third International Workshop on

Conference_Location

Suzhou, Jiangsu

Print_ISBN

978-1-4244-6334-3

Type

conf

DOI

10.1109/IWACI.2010.5585154

Filename

5585154