Prediction and Realization of DO in Sewage Treatment Based on Machine Vision and BP Neural Network
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.
Author: Liu Liping, Sunjin
Sheng, Yin Jing-tao, Liang Na
Journal Code: jptkomputergg140110
