Prediction and Realization of DO in Sewage Treatment Based on Machine Vision and BP Neural Network

Liu Liping (Hebei United University)
Sunjin Sheng (Hebei United University)
Yin Jing-tao (Hebei Energy College of Vocation and Technology)
Liang Na (Hebei United University)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 12, No 4: December 2014

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.