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Yin Jing-tao
Hebei Energy College of Vocation and Technology
Computer Science & IT

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Prediction and Realization of DO in Sewage Treatment Based on Machine Vision and BP Neural Network Liu Liping; Sunjin Sheng; Yin Jing-tao; Liang Na
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 4: December 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i4.437

Abstract

Dissolved Oxygen (DO) is one of the most important parameters describing biochemical process in wastewater treatment. It is usually measured with dissolved oxygen meters, and currently galvanic and polarographic electrodes are the predominant methods. Expensive, membrane surface inactivation, and especially need of cleaning and calibrating very frequently are common disadvantages of electrode-type measuring sensors. In our work, a novel method for Prediction and Realization dissolved oxygen based-on Machine Vision and BP Neural Network was researched. Pictures of the water-body surface in aeration basins are captured and transformed into HSI space data. These data plus the correspondent measured DO values are processed with a neural network. Using the well-trained neural network, a satisfied result for classifying dissolved oxygen according to the water-body pictures has been realized.
Co-Authors Liang Na Liu Liping Sunjin Sheng