Title :
Biological oxygen demand (BOD) soft measuring based on dynamic neural network (DNN): A simulation study
Author :
Han Honggui ; Qiao Junfei
Author_Institution :
Coll. of Electron. & Control Eng., Beijing Univ. of Technol., Beijing, China
Abstract :
Rapid, accurate and reliable measurements of biological oxygen demand (BOD) are a key basis for monitoring and controlling wastewater treatment processes (WWTP). A kind of soft measurement based on the dynamic neural network (DNN) is proposed in this paper, which can be used to monitor and model the important parameters of the wastewater treatment process on-line. The main parts of the soft measurement are: soft measurement model design and dynamic neural network design. The results of the simulations demonstrate that this soft measurement can measure the BOD concentration on-line, it is provided with real-time ability, good stability, and high precision and so on.
Keywords :
biological techniques; chemical engineering computing; computerised monitoring; neural nets; oxygen; wastewater treatment; biological oxygen demand; dynamic neural network; dynamic neural network design; on-line monitoring; soft measurement model design; soft measuring; wastewater treatment process; Artificial neural networks; Biological control systems; Biological system modeling; Board of Directors; Monitoring; Neural networks; Pollution measurement; Power system modeling; Process control; Wastewater treatment;
Conference_Titel :
Asian Control Conference, 2009. ASCC 2009. 7th
Conference_Location :
Hong Kong
Print_ISBN :
978-89-956056-2-2
Electronic_ISBN :
978-89-956056-9-1