DocumentCode :
3325572
Title :
Application of neural networks in online oil content monitors
Author :
He, Li-Ming ; Kear-Padilla, Lora L. ; Lieberman, Stephen H. ; Andrews, John M.
Author_Institution :
San Diego State Univ. Found., CA, USA
Volume :
2
fYear :
2001
fDate :
2001
Firstpage :
1404
Abstract :
Four major types of oils were examined to obtain both fluorescence and light scattering spectra as a function of oil concentrations. The large variations in fluorescence and scattering intensity with oil types and sub-types make it difficult to calibrate the analytical instrument using traditional methods. We implemented a multivariate, nonlinear calibration of instrumental response through an artificial neural network. We demonstrated that the combined use of fluorescence and scattering data significantly improves the quantitative prediction accuracy. The trained backpropagation neural network was used successfully to predict the concentrations of single oils and their mixtures, and appears well suited for the calibration of an online oil content monitor. The newly developed technique permits the online monitoring of oil concentrations in wastewater discharged from ships and oil refinery industry
Keywords :
backpropagation; calibration; computerised monitoring; environmental science computing; light scattering; neural nets; real-time systems; water pollution control; backpropagation; calibration; environmental sciences; fluorescence; light scattering spectra; neural networks; oil concentration; oil content monitoring; real time systems; wastewater; Accuracy; Artificial neural networks; Backpropagation; Calibration; Fluorescence; Instruments; Light scattering; Monitoring; Neural networks; Petroleum;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-7044-9
Type :
conf
DOI :
10.1109/IJCNN.2001.939567
Filename :
939567
Link To Document :
بازگشت