Research for Biofouling Detection Based on Optical Fiber Self-Referencing Technique and ANN

Article Preview

Abstract:

More attention is paid to on-line monitoring of biofouling in industrial water process systems. Combined optical fiber self-referencing technology with artificial neural networks (ANN) technology, real time detection technique for forming thickness and ingredient is put forward, which provides technical support and reliable data for analyzing biofouling influencing factors, contaminant separation and warning. Unclad fiber sensing mechanism and self-referencing fiber optic sensor are presented. Compensation technology based on radial basis function (RBF) neural network and learning algorithm are studied in order to solve the problem of measurement precision and range. Biofouling forming and optical characteristics in industrial real water systems are researched. A new method is provided for the research on biofouling in real water system, which can be used in other fields such as mining, environment protection, medical treatment and transportation of oil, gas and water.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 605-607)

Pages:

1965-1971

Citation:

Online since:

December 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Jason C. Quirin, John M. Torkelson. Self-referencing Fluorescence Sensor for Monitoring Conversion of Nonisothermal Polymerization and Nanoscale Mixing of Resin Components. Polymer. 2003,44:423~432

DOI: 10.1016/s0032-3861(02)00780-2

Google Scholar

[2] Jane Hodgkinson, Mark Johnson, John P. Dakin. Comparison of Self-referencing Techniques for Photothermal Detection of Trace Compounds in Water. Sensors and Actuators B. 2000, 67:227~234

DOI: 10.1016/s0925-4005(00)00419-6

Google Scholar

[3] Gerde, Jose A, Hardy Connie L, Hurburch Jr., Charles R. et al. Rapid Determination of Degradation in Frying Oils with Near-Infrared Spectroscopy. Journal of the American Oil Chemists' Society, 2007, 84(6):519~522

DOI: 10.1007/s11746-007-1068-y

Google Scholar

[4] Szustakowski M., Ciurapinski W., Palka N. Recent development of fiber optic sensors for perimeter security. IEEE Annual International Carnahan Conference on Security Technology. 2001:142~148

DOI: 10.1109/.2001.962826

Google Scholar

[5] Qijiang Li; Juanjuan Yan; and Zheng Zheng. Performance improvement of a coherent optical fiber communication system with the phase estimation algorithm. Optoelectronics Letters. 2010, 6(1):27~30

DOI: 10.1007/s11801-010-9126-3

Google Scholar

[6] Leng J. S., Asundi A. NDE of smart structures using multimode fiber optic vibration sensor. NDT and E International. 2002, 35(1):45~51

DOI: 10.1016/s0963-8695(01)00024-x

Google Scholar

[7] Sitao Wu, Tommy W. S. Chow. Induction Machine Fault Detection Using SOM-Based RBF Neural Networks. IEEE Transactions on Industrial Electronics. 2004,2(1):183~194

DOI: 10.1109/tie.2003.821897

Google Scholar

[8] Wei Zhao, Ye San. RBF neural network based on q-Gaussian function in function approximation. Frontiers of Computer Science. 2011:1~6

DOI: 10.1007/s11704-011-1041-7

Google Scholar