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Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received August 12, 2009
Accepted October 9, 2009

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Sludge settleability detection using automated SV30 measurement and comparisons of feature extraction methods

Yejin Kim Hoonsik Yeom Soojung Choi Hyeon Bae¹ Changwon Kim^†

Department of Environmental Engineering, Pusan National University, Busan 609-735, Korea ¹Busan Techno-Park, Busan, Korea

cwkim@pusan.ac.kr

Korean Journal of Chemical Engineering, March 2010, 27(3), 886-892(7), 10.1007/s11814-010-0139-1

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Abstract

The need for automation and measurement technologies to detect the process state has been a driving force in the development of various measurements at wastewater treatment plants. While the number of applications of automation & measurement technologies to the field is increasing, there have only been a few cases where they have been applied to the area of sludge settling. It is not easy to develop an automated operation support system for the detection of sludge settleability due to its site-specific characteristics. To automate the human operator’s daily test and diagnosis work on sludge settling, an on-line SV30 measurement was developed and an automated detection algorithm on settleability was developed that imitated heuristics to detect settleability faults. The automated SV30 measurement is based on automatic pumping with a predefined schedule, the image capture of the settling test with a digital camera, and an analysis_x000D_ of the images to detect the settled sludge height. To detect settleability faults such as deflocculation and bulking from these images, two feature extraction methods were used and their performance was evaluated.

Keywords

Detection Diagnosis Dynamic Time Warping Discriminant Analysis Settling Sludge Volume Index

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