NDLI: Researches of intelligent control system for the sludge activity in the aeration tank of wastewater treatment

Content Provider	IEEE Xplore Digital Library
Author	Shaode Zhang Yinrong Huang Baohe Liu
Copyright Year	2011
Description	Author affiliation: School of Electrical Engineering & Information Anhui University of Technology, Maanshan, Anhui Province, China (Shaode Zhang; Yinrong Huang) \|\| School of Architecture Engineering Anhui University of Technology, Maanshan, Anhui Province, China (Baohe Liu)
Abstract	The level of the microbial activity of the activated sludge determined the efficiency of the activated sludge wastewater treatment essentially. An intelligent optimal control system in the nature of the best activity of the activated sludge is constituted in this paper. According to the influent water quality, constituted a MIMO-LSSVM soft measurement model to predict the sludge activity with a variety of physical and chemical parameters such as the influent conditions, and took the activity of the activated sludge as a feedback signal, and used fuzzy neural networks to optimize the dissolved oxygen and sludge density setting value. Finally, the inverse control system based on least squares support vector machine was used to decouple and track the setting value of dissolved oxygen density and sludge density. In this paper, Under the constraints of achieving the best activity of the activated sludge, this method not only ensuring the stability of water quality, but also reducing power consumption significantly and improving the energy efficiency of wastewater treatment effectively.
Starting Page	756
Ending Page	762
File Size	322404
Page Count	7
File Format	PDF
ISBN	9781424487370
e-ISBN	9781424487387
DOI	10.1109/CCDC.2011.5968283
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least Squares Support Vector Machine (LSSVM) Biological system modeling Predictive models soft measurement Control systems Wastewater treatment Microorganisms optimize setting value Dissolved Oxygen(DO) inverse control sludge activity Energy efficiency Mathematical model
Content Type	Text
Resource Type	Article

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