Soft sensor modeling of AlCl3·6H2O content based on Powell-BP

Author

Yang, Jie ; Gao, Zengliang ; Liu, Yi

Author_Institution

Inst. of Process Equip. & Control Eng., Zhejiang Univ. of Technol., Hangzhou, China

Volume

2

fYear

2010

fDate

6-7 March 2010

Firstpage

413

Lastpage

416

Abstract

The production period of the crystalline aluminium chloride is considerably long. However, the offline assay of AlCl₃Â·6H₂O content has large time delay. Thus soft sensor modeling is needed to analyze its content, and estimate the value to improve the product quality. The conventional back-propagation (BP) neural network training is easily trapped to the local minimum, To overcome this embarrassment, the Powell method is adopted to optimize the training algorithm, improve the method of extremum computing, and reduce the difficulty of network realization. Finally, the soft sensor model of AlCl₃Â·6H₂O is established. Test results show that, compared with conventional neural network algorithms, the prediction of Powell-BP is more accurate and the convergence time is shorter.

Keywords

backpropagation; chemistry computing; learning (artificial intelligence); manufacturing processes; neural nets; optimisation; production engineering computing; AlCl₃; H₂O; Powell-BP method; backpropagation; crystalline aluminium chloride; neural network training; production period; soft sensor modeling; Aluminum; Computer networks; Convergence; Crystallization; Delay effects; Neural networks; Optimization methods; Prediction algorithms; Production; Testing; BP neural network; Powell method; crystalline aluminium chloride; soft sensor;

fLanguage

English

Publisher

ieee

Conference_Titel

Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on

Conference_Location

Wuhan

ISSN

1948-3414

Print_ISBN

978-1-4244-5192-0

Electronic_ISBN

1948-3414

Type

conf

DOI

10.1109/CAR.2010.5456584

Filename

5456584