Research on Fault Identification of Sewage Treatment Based on PSO Clustering Algorithm


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Various unusual conditions are likely to occur during sewage treatment process, which would lead to some consequences such as the decrease of water quality in the process of sewage treatment and the increase of disposal process, whereby causing a great influence to the practical operation efficiency of sewage treatment factories. Based on the analysis of the fault characteristics during the process of active sludge sewage treatment, a PSO clustering algorithm is presented. By putting this algorithm into the fault classification in sewage treatment, the results demonstrate that this algorithm could be an effective identification towards the unusual conditions during the sewage treatment process, which provides an efficient way for sewage treatment process fault diagnosis.



Edited by:

Guodong Zhang and Shengguo Cheng




R. N. Li et al., "Research on Fault Identification of Sewage Treatment Based on PSO Clustering Algorithm", Advanced Materials Research, Vol. 599, pp. 622-627, 2012

Online since:

November 2012




[1] Pu wenhong. Experts system research based on the sewage treatment process [J]. Industrial Water Treatment, 2007, 27(5): 23-25. in Chinese.

[2] Li xiaodong, Zeng guangming, Jiang ru. Improved support vector machine fault identification of the operational status of the sewage treatment plant[J]. Hunan university journal(natural science), 2007, 34(12): 68-70. in Chinese.

[3] Cao xiaoli, Jiang chaoyuan, Gan siyuan. Clustering support vector machine-based marine sewage treatment plant fault identification [J]. Computer Applications, 2008, 28 (10): 2648-2651. in Chinese.


[4] Shi changhan, Wang yuqian. Expert system of sewage treatment plant fault identification [J]. Water Supply and Sewerage, 2001, 27(8): 85-90. in Chinese.

[5] Wen min, SVM-Fuzzy ES. Technology in the sewage treatment process fault identification [A]. Master's degree thesis, 2006. in Chinese.

[6] Duan haibin. Ant colony algorithm and its application [M]. Beijing: Science Press, 2005 . in Chinese.

[7] Wan jiewen. Li hean. Particle Swarm Optimization Algorithms [J]. Modern computer, 2009 (301) : 22-27. in Chinese.

[8] OMRANG M, SALMAN A, ENGELBRECHT A P. Image classification using particle swarm optimization [C]/Proc of the 4th Asia-Pacific Conference on Simulated Evolution and Learning. 2002: 370-374.

[9] Yang wei, Li qiqiang. The total number of particle swarm optimization [J]. Engineering Science, 2004, 6 (5) : 87-94. in Chinese.

[10] MERWE D W van der, ENGELBRECHT A P. Date clustering using particle swarm optimization [C] /Proc of IEEE Congress on Evolutionary Computation. 2003: 215-220.